To Act or React: Investigating Proactive Strategies For Online Community Moderation

06/27/2019
by   Hussam Habib, et al.
0

Reddit administrators have generally struggled to prevent or contain such discourse for several reasons including: (1) the inability for a handful of human administrators to track and react to millions of posts and comments per day and (2) fear of backlash as a consequence of administrative decisions to ban or quarantine hateful communities. Consequently, as shown in our background research, administrative actions (community bans and quarantines) are often taken in reaction to media pressure following offensive discourse within a community spilling into the real world with serious consequences. In this paper, we investigate the feasibility of proactive moderation on Reddit -- i.e., proactively identifying communities at risk of committing offenses that previously resulted in bans for other communities. Proactive moderation strategies show promise for two reasons: (1) they have potential to narrow down the communities that administrators need to monitor for hateful content and (2) they give administrators a scientific rationale to back their administrative decisions and interventions. Our work shows that communities are constantly evolving in their user base and topics of discourse and that evolution into hateful or dangerous (i.e., considered bannable by Reddit administrators) communities can often be predicted months ahead of time. This makes proactive moderation feasible. Further, we leverage explainable machine learning to help identify the strongest predictors of evolution into dangerous communities. This provides administrators with insights into the characteristics of communities at risk becoming dangerous or hateful. Finally, we investigate, at scale, the impact of participation in hateful and dangerous subreddits and the effectiveness of community bans and quarantines on the behavior of members of these communities.

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1. Introduction

Reddit, the self-proclaimed “front page of the internet”, has over 138K active communities, called subreddits, with over 330M active users (Red, 2019d). In recent years, the site has been mired in controversies around the role that its communities played in originating and propagating sexist, racist, and generally hateful online socio-political discourse. A few of the recent controversies have involved communities such as: r/Physical_Removal (banned in 8/2017) which advocated for the physical removal of liberals in the United States prior to and even after the murder of Heather Heyer in Charlottesville (Collins, 2017b), r/incels (banned in 11/2017) which endorsed and celebrated the murder of and violence against sexually active women (Collins and Zadronzny, 2018), r/greatawakening (banned in 3/2018) and r/pizzagate (banned in 11/2016) which falsely alleged the existence of child trafficking rings by the US Democratic Party and left-wing corporations resulting in real-life attacks, threats, and harrassment (e.g., against the Comet Ping Pong restaurants (Ohlheiser, 2016)), r/WatchPeopleDie (banned in 3/2019), r/gore (banned in 3/2019) which disseminated videos of the Christchurch Mosque shootings (Stephen, 2019), and r/The_Donald (active as of 4/2019) which continues to pedal white genocide conspiracy theories (Ward, 2018), false-flag theories in the wake of shootings and bomb threats (Rose, 2018), and violent anti-immigrant (Lagorio, 2018) and anti-Islam rhetoric (Breland, 2019).

In reaction to many of these controversies, Reddit has resorted to banning or quarantining subreddits citing violations of the Reddit content policy (Red, 2019c) which prohibits specific types of content including content which “encourages or incites violence”. However, the effectiveness and timeliness of such bans and quarantines is frequently debated. While previous research (Chandrasekharan et al., 2017) concluded that such bans “worked for Reddit”, others have pointed out that they are too reactionary and occur only after a significant amount of damage has already been observed (Morse, 2019; Red, 2017; Marantz, 2018). Along another dimension, Reddit has also faced criticism for inconsistent and seemingly ad-hoc applications of the content policy by those claiming that the platform provides a safe-haven for extremist ideologies and others claiming that the platform leverages the content policy as a mechanism to censor “non-mainstream” opinions and ideologies.

Despite these arguments about how Reddit should (not) be moderated, little is actually known about how subreddits evolve over time, the evolutionary predictors of offensive or dangerous subreddits, and how moderation decisions impact the wider community. This gap in knowledge presents Reddit administrators with several challenging questions: how to identify offensive or dangerous subreddits before serious harm has been caused by them, how to moderate the impact of these subreddits on users and other subreddits, and how to scientifically rationalize these moderation decisions. In this paper, we seek to answer these questions. We do so by setting out to test the following hypotheses.

H1. Subreddits may not converge to topical or user base stability. (Section 3) This hypothesis, if valid, will show that subreddits need to be constantly monitored for changing discourse and moderation decisions need to be re-evaluated from time to time – an expensive proposition for a small number of human moderators and administrators. If invalid, the failed hypothesis will show that subreddits always converge to topical and user base stability and therefore only need to be evaluated by moderators once – after topical or community stability has been reached. In order to test this hypothesis, we develop techniques to track the nature and magnitude of subreddit evolution. These techniques permit us to quantify the distance between two subreddits and also identify subreddits with similar evolutionary behaviors.

H2. Evolution into hateful or dangerous subreddits can be predicted. (Section 4) This hypothesis, if valid, will show that tools may be built to help moderators pre-emptively identify subreddits likely to devolve into hateful or dangerous subreddits. If invalid, the failed hypothesis will show that moderators cannot perform pre-emptive actions to mitigate the impact of offensive or dangerous subreddits. In order to test this hypothesis, we develop explainable machine learning techniques to identify the value of community-, user-, moderator- and structure-based features in predicting the evolutionary outcome of a subreddit.

H3. User participation in a hateful subreddit negatively changes the nature of their participation within the broader community and this can be corrected by bans or quarantines. (Section 5) This hypothesis, if valid, will show that banning or quarantining hateful subreddits is an effective way to curb their impact on the broader community. If invalid, the failed hypothesis will point to the need for more nuanced techniques to moderate the impact of hateful communities. In order to test this hypothesis, we perform large-scale analysis of the impact of join, community ban, and quarantine events on user behavior.

2. Reddit: The Platform and Dataset

Reddit is currently the sixth most popular site in the USA with over 330M active monthly users (Red, 2019d). In recent years, Reddit has come under increasing criticism for the types of content shared by its users (Ward, 2018; Rose, 2018; Lagorio, 2018; Breland, 2019). This has resulted in numerous changes of the Reddit content policy and moderation strategies. In this section, we provide a high-level overview of Reddit with a focus on its content moderation policies (Section 2.1) and our datasets (Section 2.2).

Content is prohibited if it
is illegal
is involuntary pornography
is sexual or suggestive content involving minors
encourages or incites violence
threatens, harasses, or bullies or encourages others to do so
is personal and confidential information
impersonates someone in a misleading or deceptive manner
uses Reddit to solicit or facilitate any transaction gift involving certain goods and services
is spam
Table 1. Prohibited content according to Reddit’s content policy. (Red, 2019c)

2.1. An overview of the Reddit platform

Month/Year Event Admin actions
02/2015 r/TheFappening gains media attention for facilitating distribution of leaked celebrity nudes. (Kosur, 2015; Spata, 2014; McKinney, 2014) Content policy amended to prohibit “involuntary pornography” and r/TheFappening is banned citing this policy. (Fap, 2014, 2015)
11/2016 r/pizzagate gains media attention after the Comet Ping Pong restaurant in Washington DC begins receiving threats in response to the conspiracy alleging the existence of a child trafficking operation by Democratic politicians. (Ohlheiser, 2016; Kang, 2016) r/pizzagate is banned citing policy about “posting personal and confidential information” and “threatening, harassing, and bullying others”. (Ohlheiser, 2016)
08/2017 r/Physical_Removal gains media attention for advocating and celebrating violence against “liberals” and Democrats in the wake of the death of Heather Heyer in Charlottesville. (Collins, 2017a). r/Physical_Removal is banned citing policy about “threatening, harassing, and bullying others”. (Lagorio-Chafkin, 2017; Collins, 2017c)
10/2017 Prolific r/The_Donald user is charged with murder of father over dispute about participation in Nazi communities. (Kranz, 2017) Content policy amended to prohibit “encouraging or inciting violence” and many subreddits including r/Nazi, r/EuropeanNationalism, and r/NationalSocialism are banned citing this new policy. (Robertson, 2017; Naz, 2017)
11/2017 r/incels gains media attention due to the subreddit’s encouragement of violence against women. (Tait, 2017; Caffier, 2017; Williams, 2017) r/incels is banned citing policy about “encouraging or inciting violence”. (Kini, 2017; Inc, 2017)
02/2018 r/Deepfakes gains media attention for facilitating the distribution of AI generated pornography involving popular actresses. (Hathaway, 2018; Cole, 2018; Ehrenkranz, 2018) r/Deepfakes is banned. Content policy amended to include deepfakes in “involuntary pornography”. (DF-, 2018; Morris, 2018)
03/2018 Parkland Florida school shooting brings debates on gun violence to the forefront and articles from 2014 which highlight the thriving gun trade on Reddit begin to emerge. (Follman and Harkinson, 2018) Content policy amended to prohibit “transactions involving certain goods” and many subreddits including r/GunDeals and r/GunsForSale are banned citing this new policy. (Gun, 2018)
04/2018 r/Braincels, a spin-off of the banned r/incels community, gains media attention for praising the actions of Alek Minassian – alleged perpetrator of the Toronto van attacks. (Janik, 2018; Ohlheiser, 2018; Wendling, 2018) r/Braincels and r/Mindcels are banned while r/TheRedPill is quarantined for “encouraging and inciting violence”. (Bra, 2018)
03/2019 r/gore and r/WatchPeopleDie gain media attention for facilitating the distribution of videos of the Christchurch shootings. (NZ-, 2019b; Mac, 2019) r/gore and r/WatchPeopleDie are banned citing policy about “encouraging or inciting violence”. (NZ-, 2019a)
Table 2. Reactionary administration: A sample of recent events which resulted in administrative changes to content policy and reactionary bans of subreddits long known to violate the content policy.

Redditors and subreddits. At a high-level, Reddit is a content aggregation platform where users, also called redditors, share content on topical forums called subreddits. Subreddits are generally formed around specific topics and can range from broad (e.g., r/politics which focuses on US politics) to extremely niche (e.g., r/birdswitharms which focuses on photoshopped images of birds with human arms). There are currently 138K active subreddits (Red, 2019d) which contain content posted by redditors. In addition to posting content, redditors can also interact with each other by commenting on posts and replying to other comments.

Democratized content-curation. Unlike other social and aggregation platforms, Reddit relies on its users for more than content generation and propagation. Redditors also play the role of content curators. Redditors may also curate content by up- or down-voting comments and posts. Content (comments or posts) with a high net-vote total is, by default, given very high visibility. For example, the comments (on a post) and posts (in a subreddit) are, by default, ordered by decreasing net-vote total. This mechanism lets users decide which comments and posts are most visible to the rest of the community and which contributions are silenced or hidden.

Decentralized moderation. Besides letting every redditor curate content by way of voting, Reddit also allows its users to create and moderate subreddits. Subreddit moderators typically choose their own fellow moderators from within the community, with a few exceptions for newly created communities and cases where there are no volunteers within the community. Subreddit moderators are tasked with setting and enforcing the rules of engagement within a subreddit. Moderators may enforce rules via the use of user bans and content deletion. However, the actions performed by the subreddit moderators do not impact redditors outside of that subreddit (e.g., a subreddit moderator cannot enforce site-wide bans).

Centralized administration. In addition to relying on volunteering community members to moderate their own subreddits, Reddit also employs administrators to set and enforce site-wide policies for content and user engagement. These policies are mandatory and applied in addition to a subreddit’s own policies. Administrators have the ability to: (1) ban users from making posts or comments visible to the rest of the platform (this is referred to as a shadow ban), (2) prevent subreddits from appearing on the Reddit front-page and in search results (this is referred to as a quarantine), and (3) ban subreddits from the platform. While these actions are available for use in response to egregious or repeated violations of the Reddit content policy, they are rarely applied except in reaction to media pressure or catastrophic external events occurring as a result of discourse on the platform. Instead, Reddit administrators encourage users unhappy with certain communities to create their own communities which enforce their preferred rules and norms (Aue, 2014). This policy of reactionary administration has been the subject of much criticism in the wake of recent controversies on Reddit.

The reactionary content policy. The Reddit content policy defines content which is acceptable for posting on the platform. Failing to adhere to this policy may result in the moderator or administrator actions described above. Prohibited content according to the April 2019 content policy is shown in Table 1. In addition to prohibiting content, Reddit also regulates content containing nudity, pornography, or profanity by requiring them to be tagged as Not Safe For Work (NSFW). Additions to the policy have been made, in large part, in reaction to negative media coverage of certain events on the platform. We illustrate several of these events and the reactionary administrative actions caused by them in Table 2.

Dataset Category
3K 22M 203M 3B Most active.
38 1M 5M 27M Banned or quarantined.
118 4M 9M 141M Hateful.
152 7M 16M 353M Related to and .
Table 3. Summary of datasets analyzed in this paper. is the number of subreddits in the dataset, is the number of unique users observed, is the number of posts, and is the number of comments in the selected subreddits. All datasets only contain activity between 01/2015 and 10/2018.

2.2. The Reddit dataset

In total, Reddit consists of over 3.5B comments on over 300M posts from over 30M unique users on 1.2M subreddits over the period from 2007-2019. In this paper, however, we focus on a subset of the entire platform. Specifically, we perform all the analysis shown in the remainder of this paper on four specific categories of subreddits described below. These are also summarized in Table 3. All the datasets studied in this work were gathered using the publicly available Reddit BigQuery dataset (Red, 2019a, b).

Most active subreddits (). We select the 3K subreddits which on average had the most number of user posts per month, during the period from 01/2015 - 10/2018. These subreddits account for 6% of all posts and 11% of all comments made on Reddit during this period. We rely on this dataset to understand the evolution and life-cycle of a typical popular subreddit.

Banned or quarantined subreddits (). We select 38 subreddits which were banned or quarantined during the period from 01/2015 - 10/2018. Examples of subreddits in this group include: r/Physical_Removal (banned in 8/2017) and r/WatchPeopleDie (banned in 3/2019). We use this dataset to understand the evolution and life-cycle of subreddits confirmed to violate the content policy.

Hateful subreddits (). We select 118 subreddits that have been frequently reported by redditors and the media for violating the Reddit content policy, yet have not been banned or quarantined by administrators. This list is compiled by analyzing the r/againsthatesubreddits and r/SubredditDrama to identify the most frequently user-reported subreddits. Additional subreddits are manually added to this list based on media reports. Examples of subreddits in this category include r/metacanada and r/KotakuInAction. This dataset was used to understand the evolution and life-cycle of frequently reported subreddits.

Subreddits related to and (). Finally, we extracted subreddits deemed to be similar to those in and

. Similarity between subreddits was measured by computing the cosine similarity of the latent vectors associated with each subreddit. We explain this process in more detail in

Section 3.1. In total, this dataset contains 152 subreddits including r/AskTrumpSupporters and r/SocialJusticeInAction. This dataset was used to understand the evolution and life-cycle of subreddits likely to share similar characteristics to banned and dangerous subreddits.

Data preprocessing. In this work, we seek to understand how subreddit characteristics evolve and if these characteristics can be predictors of dangerous behavior or hatefulness. Therefore, we need to consider how subreddit states change at different points in time. We achieve this by breaking down our data for each subreddit into one-month slots (starting from 1/2015 until 10/2018) and individually analyzing the characteristics of these “subreddit states”.

3. Subreddit Evolution and Convergence

In this section, we focus on testing the following hypothesis: H1. Subreddits may not converge to topical or user-base stability. If valid, this hypothesis demonstrates: (1) the need for techniques which can monitor subreddit evolution and frequently evaluate the suitability of making moderation decisions and (2) the potential for identifying evolutionary patterns that can serve as early predictors for subreddits likely to require future administrator interventions for offensiveness. If invalid, the failed hypothesis will show that subreddits converge to topical or user base stability and therefore only need to be evaluated by moderators once – after stability has been reached. Our methods and results are outlined in Table 4.

Methodological questions Section 3.1
How do we convert subreddit states into fixed-length vectors representing topics and active users? Section 3.1.1
How do we compute the topical or user base distance between two subreddit states? Section 3.1.2
Research questions Section 3.2
How much do subreddit topics and user bases evolve per month on average? Section 3.2.1
How do subreddit topics and user bases evolve as a function of subreddit age? Section 3.2.2
Do subreddits converge to topical or user base stability? Section 3.2.3
Table 4. Hypothesis 1: Subreddits may not converge to topical or user stability. Summary of methods and results.

High-level overview. Our goal is to identify if subreddits reach topical or user stability – i.e., if the topics of discussion and users participating in these discussions stabilize over time. In order to accomplish this goal, we need a technique to quantify the (dis)similarity in topics and user bases for a subreddit at two points in time. We accomplish this using the following approach, which is explained in more detail in Section 3.1. First, for each month of activity in each subreddit (i.e., for each subreddit state), we generate a pair of vectors – a vector representative of the topics being discussed in posts and comments in the subreddit (i.e., a topic vector) and a vector representative of active users in the subreddit (i.e., an active user vector) using Latent Similarity Analysis (LSA) (Landauer et al., 1998)

. Second, we perform hierarchical clustering on both types of vectors across all the subreddit states in our dataset. We then quantify the

evolutionary distance of topics or active users between any two states of a specific subreddit as the height of the nearest common parent in our topic or active user clustering model, respectively.

We claim that the topics or user base of a subreddit () has converged between times and if the evolutionary distance between the corresponding vectors at these timestamps is minimum. We present our results, which broadly (i.e., for all categories of subreddits in our study) validate the proposed hypothesis in Section 3.2.

3.1. Methods

Our method for testing the hypothesis involves two steps: First, we create summaries, in the form of latent vectors, of topics and user participation for each subreddit state (Section 3.1.1). Next, we measure the similarities of these summaries as a function of time (Section 3.1.2).

3.1.1. How do we convert subreddit states into fixed-length vectors representing topics and active users?

We generate two types of latent vectors for each subreddit state – a topic vector and an active user vector. These are generated as follows.

Topic vectors. For each subreddit state in our dataset, we randomly sampled 10% of all comments and used these to generate topic vectors. A 10% sample was used to mitigate the infeasibility of efficiently processing billions of comments. Each sampled comment was pre-processed by removing English-language stop words, tokenizing, and stemming. A count vector, which keeps count of the number of occurrences of each tokenized and stemmed word, was then generated for each subreddit state. With these count vectors, we created a matrix in which each row is associated with one subreddit state in our dataset and each column is associated with a unique stemmed token. The cell represents the number of times the token was observed in our sampled comments obtained from the subreddit state. However, since simple frequency counts are not discriminative, we follow standard recommendations from Jurafsky and Martin (Jurafsky, 2000) and apply PPMI (Positive Pointwise Mutual Information) weighting to each word. PPMI (Bullinaria and Levy, 2007; Fano, 1963) draws on the intuition that the association between two words should be weighted by the difference of how often they actually co-occur in comparison to how much we would expect them to co-occur if they were truly independent. We then view each row as the topic vector associated with a subreddit state.

Active user vectors. We used a similar approach as above to encode the active participants in each subreddit state with only a few minor changes. First, for each subreddit state in our dataset, we identified the set of unique and active user observed. Second, we selected a subset of reference subreddit states which served as the reference points used to compare all other subreddits – i.e., we considered how all other subreddit states appeared with respect to these reference points. In our study, we selected all subreddits with between 5K-15K active users as the reference set. Next, we created an active user co-occurrence matrix where represented the number of common active users between subreddit state and reference subreddit state

. Similar to our creation of topic vectors, we applied PPMI on this raw co-occurrence matrix to uncover information about highly co-occurring cohorts. Finally, since our matrix was sparse and high-dimensional, we performed dimensionality reduction by running PCA (Principle Component Analysis) and selected the top 750 principle components. We selected 750 components since it offered the best trade-off between dimension reduction and dataset variance loss – i.e., we only saw a

1% reduction in dataset variance and 62.5% reduction in dimensions. We then view selected row in this 750-dimension matrix as the active user vector associated with a subreddit state.

We note that both the methods described above essentially create representations of different subreddit states in the same -dimensional space. A similar approach was used in previous work seeking to uncover the commonalities between r/The_Donald and other communities (Martin, 2017) using vector algebra.

3.1.2. How do we compute the evolutionary distance between two subreddit states?

Given succinct vector representations of subreddit states, we now need to quantify the distance between these representations. A simple approach is to rely on euclidean distances between the vectors of two subreddit states, however, there are two main drawbacks with this approach: (1) it is not clear what a euclidean distance threshold must be for us to claim that subreddit states have converged and (2) the euclidean distance metric is not suitable for measuring the nature of subreddit evolution – i.e., the direction (relative to other communities) of evolution in terms of topics and active user bases. We overcome these limitations by relying on agglomerative cluster distance as a measure of distance between subreddit states.

Figure 1. An snippet of a larger dendrogram obtained by hierarchical clustering on topic vectors. LDA topics are associated with each cluster.

Creating subreddit state clusters. Using the topic and active user vectors generated in our previous step, we generated two clustering models – one which clustered subreddit states by their topic vectors and another which clustered subreddit states by their active user vectors. In both cases, we used agglomerative (bottom-up) hierarchical ward-method clustering (Murtagh and Legendre, 2014). Agglomerative clustering starts off by treating each subreddit state individually and merging closest clusters together in a step by step process. Typically visualized as a dendrogram, clusters at higher levels are larger and more general while clusters at lower levels are much smaller and specific. Agglomerative clustering, in addition to providing an intuitive notion of inter-cluster distance (described below), also provides the ability to cluster without pre-specifying number of clusters.

Quantifying magnitude of evolution. Given our hierarchical cluster models based on topic and active user vectors, we used an intuitive measure of similarity between two subreddit states. We quantified the distance between subreddit states and as the height of the nearest common ancestor of and . To compute this distance, we performed a search for the nearest common ancestor of the input subreddit states, computed the of the number of nodes between this parent and each of the subreddit states being compared (i.e.,  and ). Therefore, if the distance between the subreddit states associated with two consecutive months of a subreddit is high, it is indicative that the (topic or active user) vectors associated with the newer subreddit states are much more similar to other subreddits than its own previous state. This is indicative of topical or user base evolution towards other subreddits. We expect that the evolutionary distance between consecutive months of a subreddit will be one or two (i.e., nearest parent is at distance one or two from each state) in the event of convergence.

Measuring nature of evolution. In addition to quantifying the magnitude of evolution, we also present a method to measure the nature of change between consecutive subreddit states. For this, we relied on Latent Dirichlet Allocation (LDA) to assign topics associated with each cluster in our hierarchical cluster models. For each subreddit state, we obtained the text of its subreddit wiki. This text was then associated with all parents of this subreddit state in our hierarchical model. LDA was then performed on the text associated with every possible cluster in our model, therefore giving us a set of topics associated with every cluster. We also repeated this process with the comments associated with each cluster. These topics give us an insight into the topical direction in which subreddits are evolving.

Figure 1 shows a snippet of the dendrogram associated with our topic vector based hierarchical cluster model. As leaves in the dendrogram we have our subreddit states and with each parent in the tree are topics extracted from the child subreddit wikis and comments using LDA. This snippet shows how r/The_Donald moved from being most closely associated with r/AskThe_Donald in late 2017 to being associated most closely with the 2016-versions of now banned subreddits – r/altright and r/CringeAnarchy in mid-2018. In this example, we see that r/The_Donald has evolved a distance of 3 between 08/2017 and 05/2018.

3.2. Results

We now quantify the magnitude of topical and active user base evolution for subreddits belonging to different datasets – i.e., , , , and . We specifically focus on measuring the average magnitude of evolution per month for different subreddits (Section 3.2.1) and how subreddits evolve as a function of their age (Section 3.2.2). Based on these analyses, we expect to be able to verify the hypothesis that subreddits do not always converge to user or topical stability (Section 3.2.3). Our results are illustrated in Figure 2.

(a) (log scale) Distribution of mean monthly topic evolution.
(b) (log scale) Distribution of mean monthly user base evolution.
(c) Subreddit age vs magnitude of topic evolution.
(d) Subreddit age vs magnitude of active user base evolution.
Figure 2. Characteristics of topic and active user base evolution for subreddits in , , , and . Figures (a) and (b) show the distribution of the mean magnitudes of topic and active user base evolution per month for subreddits in different categories. Figures (c) and (d) show the average magnitude of topic and active user base evolution as a function of subreddit age. The maximum lifetime of a subreddit is the time between the first post and last post in our datasets (our data collection ended on 10/2018). The ‘fraction of lifetime’ is the fraction of this maximum lifetime.

3.2.1. How much do subreddit topics and active user bases evolve per month?

We make the following observations from Figure 1(a) and Figure 1(b) which shows the distributions of mean monthly magnitude of topical and active user base evolution for subreddits in different categories.

Subreddits have a high average rate of evolution per month. Looking at the average magnitude of topical and active user base evolution per month for different subreddit categories, we see that the mean values for subreddits in different categories are between 4 and 10. This is indicative that the average subreddit evolves at a high rate on average. For reference, an average value of 1 or 2 would indicate that the subreddit has not evolved since its conception. We also notice that, in general, the magnitude of evolution of user bases is lower than of topics.

Average magnitude of evolution per month is consistent across subreddit categories, but the long tails vary. We see that subreddit categories are not a discriminator when considering only the average monthly magnitudes of evolution. However, we see that the distribution of these magnitudes varies by subreddit category. In particular, we see that subreddits in and are heavier weighted towards lower and higher average monthly magnitudes of evolution – i.e., subreddits in these groups are more likely to have extremely high or low average magnitudes of evolution. This indicates that (1) some subreddits in and show more signs of being likely to converge to topical and user base stability, but this is not a common case for any subreddit category and (2) different categories of subreddits might have different evolutionary patterns – a promising insight for hypothesis H2 which aims to predict the subreddit category based on evolutionary patterns.

In addition to measuring a subreddit’s average magnitude of evolution, we are also able to check the nature of subreddit evolution by observing the keywords of different subreddit states. We examine the most evolved subreddit in each category to understand the nature of evolution in each case. There are highlighted in Table 5. Of particular interest are r/enoughsandersspam and r/altright. Studying the nature of evolution of r/enoughsandersspam we see that the subreddit initially started as a subreddit to mock and harass supporters of US politician Bernie Sanders going as far as goading them into suicide and threatening rape (ESS, 2019b). This behavior resulted in its addition into . However, we observe that the subreddit turned into a pro-Bernie Sanders community in late 2018 as a result of a moderator takeover (ESS, 2019a). This resulted in an unusually high magnitude of active user base evolution. Similarly, we see that r/altright emerged as a political movement in early 2016 and evolved into a white supremacist and anti-Semitic subreddit by early 2017. Our method identifies these subreddits as the most evolved and is able to hint at the nature of evolution, which serves as validation for the techniques and metrics in Section 3.1.

Subreddit AMM LM Nature of evolution
() r/altright T:13.7 U:11.5 T:31 U:17 right, support, vote, jew, white, support
() r/enoughsandersspam T:10.1 U:12.7 T:50 U:53 bernie, the_donald, spam, hero, obama, original
() r/the_meltdown T:14.4 U:12.3 T:31 U:13 prison, wikileaks, revolution, DNC, propaganda
() r/GalaxyNote7 T:30.7 U:22.0 T:50 U:88 samsung, battery, nexus, deals, free, blackfriday
Table 5. Subreddits with highest monthly evolution magnitudes in each category and the nature of their evolution. AMM is the average magnitude of evolution per month, LM is the magnitude of evolution over the entire lifetime of the subreddit, T and U denote the magnitudes of topic and active user base evolution, Red keywords represent topics associated with the start of the subreddit’s lifetime, and Blue keywords represent topics associated with the end of the subreddit’s lifetime which is characterized by when the subreddit was banned or the end of our data collection (10/2018).

3.2.2. How do subreddit topics and active user bases evolve as a function of subreddit age?

Our previous result shows that there is, on average, a significant magnitude of evolution per month for subreddits in all our categories. However, owing to the long tail distributions, we are unable to conclude that subreddits do not converge to topics or user bases. We now breakdown the magnitude of evolution by subreddit age. In the event of convergence, we expect to see that the magnitude of evolution drops to and stays at the minimum (1 or 2) after a certain point in the lifetime of a subreddit. Figure 1(c) and Figure 1(d) show how subreddit topics and active user bases evolve as a function of their age. We can make the following observation from these plots.

Banned subreddits () show high magnitude evolutions before being banned. Figure 1(c) shows that the magnitude of topical evolution per month is generally high for all subreddit categories, regardless of age of the subreddit. However, across different categories we observe a difference. Particularly in the case of we see that the magnitude of evolution is significantly lower for most of the subreddit’s lifetime and a sharp increase occurs right before the subreddit is banned. We see a similar rapid increase in magnitude even when considering user base evolution (shown in Figure 1(d)). This is indicative of a rapid change in topic and community right before a ban event – unfortunately, a causal relationship cannot be inferred from our data. We also analyze the average net evolution over a lifetime for subreddits in each category. Here again we find that subreddits in and have significantly higher average magnitudes of topic evolution (14.7 and 18.4, respectively) than other subreddits ( has a mean topic evolution magnitude of 8.2). This is indicative that subreddits which get banned or get labeled as hateful often do not start with discussions or user bases which occur at the end of their lifespan.

3.2.3. Takeaway: Do subreddits converge to topical or user stability?

Taken together, our results show that subreddits generally have a high average magnitude of topic and user base evolution (per month and over a lifetime) and this evolution, in most cases, is independent of the age of the subreddit. Therefore, we are unable to claim that subreddits always converge to topical or user stability – i.e., hypothesis H1 is valid. This suggests that policies that involve verifying and applying moderation policies at the time of subreddit creation is not sufficient. Rather, the application of moderation policies need to be considered repeatedly over the course of the subreddit’s lifetime. This suggests a very high attention cost for human administrators and moderators who need to (1) monitor how thousands of subreddits are evolving and (2) identify how and when to act to maintain community civility.

4. Predictors of Hateful or Dangerous Subreddits

In addition to showing that human administrators and moderators seeking to maintain civility need to constantly monitor communities for changing topics and user bases, our previous results (Section 3.2) show that banned and hateful subreddits have different evolutionary characteristics than other subreddits. Specifically, we showed that (1) subreddits in these groups were more likely to have smaller average monthly evolution magnitudes – particularly when considering topic evolution, (2) have larger magnitudes of evolution over a lifetime, and (3) have a few high magnitude evolution events – typically in the latter half of the subreddit lifespan. All these results suggest that it might be possible to identify subreddits likely to evolve into hateful or dangerous subreddits based on measurable evolutionary characteristics. In this section, we use the above insights to test the following hypothesis: H2. Evolution into hateful or dangerous subreddits can be predicted. This hypothesis, if valid will show that tools may be built to help moderators pre-emptively identify and watch subreddits likely to devolve into offensive or dangerous subreddits. If invalid, the failed hypothesis will show that evolutionary characteristics cannot be used to motivate pre-emptive moderation actions to mitigate the impact of hateful or dangerous subreddits. Our methodological and result contributions are outlined in Table 6.

Methodological questions Section 4.1
What are hateful or dangerous subreddits? Section 4.1.1
What evolutionary features do we extract for each subreddit? Section 4.1.2
How do we identify the predictive values of features? Section 4.1.3
Research questions Section 4.2
Can we identify hateful or dangerous subreddits by their evolutionary features? Section 4.2.1
What features are most important for predicting the evolution into hateful or dangerous subreddits? Section 4.2.2
How far ahead of time can hateful or dangerous subreddits be predicted? Section 4.2.3
Can evolution into hateful or dangerous subreddits be predicted? Section 4.2.4
Table 6. Hypothesis 2: Evolution into hateful or dangerous subreddits can be predicted. Summary of methods and results.

High-level overview.

Our goal is to identify the features that are good predictors of future subreddit behavior. Specifically, we wish to identify the features that can be useful to predict the likelihood of a subreddit being classified as hateful or dangerous in the future. To achieve our goal, we first need to understand what subreddits are

hateful or dangerous. Once we have methods to assign these labels, we need a method to identify the evolutionary characteristics of subreddits which are predictors of subreddits being classified as hateful or dangerous. We accomplish this using the following approach, which is explained in more detail in Section 4.1. First, we assign hate labels to all subreddits in our dataset, dangerous labels to all subreddits in our dataset, and other labels to all subreddits in our dataset. Next, we break the lifespan of each subreddit down into four quarters and extract features from each of these periods. Our features come from four categories – community-related, moderator-related, user-related, and structure-related.

Once we have these features and labels for each subreddit, we fit several explainable classification models including a logistic regression, decision tree, and random forest. We argue that if our explainable classifiers have reasonably high accuracy in predicting the labels assigned to each subreddit, then the features that are highly weighted by them

must be good predictors for hateful and dangerous subreddits. We present our results (Section 4.2), which broadly validate our proposed hypothesis by demonstrating that (1) we can achieve high classification accuracy with relatively simple evolutionary features and (2) hateful and dangerous subreddits can be identified with reasonable accuracy very early in their lifetime.

4.1. Methods

Our method for testing the proposed hypothesis requires us three steps. First, we need a method to assign hateful, dangerous, and other labels to subreddits in our dataset (Section 4.1.1). Next, we need to identify different features to extract from our subreddits (Section 4.1.2). Finally, we evaluate the predictive value of these features (Section 4.1.3).

4.1.1. What are hateful or dangerous subreddits?

We rely on our previously described (in Section 2.2) datasets of banned and quarantined subreddits (), hateful subreddits (), and a random sample of 190 of the most active subreddits () as sources of dangerous, hateful, and other subreddits, respectively. This labeling is acceptable since subreddits from were banned or quarantined for violating one of Reddit’s content policies – a clearly unacceptable and dangerous behavior. Therefore, by analyzing the features most likely to be predictive for subreddits in , we are able to identify evolutionary characteristics which are likely to lead to violations of Reddit’s content policies. Similarly, subreddits from were found to be the most frequently reported non-banned but hateful subreddit by the wider reddit community (r/againsthatesubreddits and r/SubredditDrama) and the media for promoting sexist and racist hate speech. Therefore, by analyzing the features most likely to be predictive for subreddits in , we are able to identify evolutionary characteristics which are likely to lead to large number of community complaints. Predictive features for both and are useful for sitewide moderators and administrators to narrow down the likely causes of future content policy violations and community complaints – enabling more effective monitoring or pre-emptive moderation interventions.

4.1.2. What evolutionary features do we extract for each subreddit?

For each subreddit in our datasets, we break their lifespan into four quarters and extract features from each of these quarters. This allows us to do several things: (1) we are able to get an identical number of features from all subreddits – even if they have vastly different lifespan values, and (2) we are able to capture features from different phases in the evolution of a subreddit. Our extracted features fall in four categories – community-, moderator-, user-, and structure-related features. These were largely influenced by existing literature seeking to predict community dynamics (Section 6.1 describes and compares this work and its influence on ours). All extracted features are shown in Table 7.

Community-related features. This category of features captures the dynamics of the interactions occurring within the community – e.g., how large is the active community, how highly do community members rate each others posts, etc.

Moderator-related features. Moderators play a large rule in directing the growth and policies within each community. This category of features captures how the moderator team interacts with the community – e.g., how many posts or comments are deleted, how frequently do moderators create stickied posts, etc.

User-related features. This category of features captures characteristics of the average users within the community – e.g., how active are users, how frequently do they delete their posts?

Structure-related features. Finally, we also introduce a category of features to capture how a subreddit is connected (in terms of shared user base) to other communities – e.g., how isolated is the subreddit, what fraction of its connections are to other communities which were previously classified as hateful or banned, etc.

Category Features Type
# active unique posters Point
# active unique commenters Point
# posts Point
# comments Point
Dist. of comments & posts Quartiles
Dist. of score & posts Quartiles
Dist. of score & comments Quartiles
% of active user growth Point
Dist. of controversial score per post Quartiles
# controversial comments Point
# gilded posts Point
Community # gilded comments Point
# moderators Point
# stickied posts Point
# removed comments Point
Moderators # removed posts Point
# active months Quartiles
Users # deleted comments Quartiles
# uniquely connected communities Point
# total connections Point
# total connections to banned communities Point
# total connections to hateful communities Point
% of connections to banned communities Point
Structural % of connections to hateful communities Point
Table 7.

List of features extracted from each quarter of a subreddit’s lifespan.

4.1.3. How do we identify the predictive value of features?

Given labels for each subreddit and a set of features associated with each stage in its lifetime, we now seek to understand the predictive values of these features. We achieve this in two steps: First, we build a machine learning classifier which uses these features to predict the labels associated with each subreddit. Next, we analyze the weights associated with each feature by the classifier. Our argument is that if a classifier is able to achieve a reasonably high accuracy, then the features it weighs heavily must have some predictive value

. We focus solely on interpretable classifier models (logistic regressions, decision trees, and random forests) since we need to obtain feature weights. However, we see very similar classifier performance, when comparing our models to other non-interpretable models such as SVMs and neural networks. To evaluate the performance of each classifier we perform 10-fold cross-validation and report the accuracy and F1-scores. Once we have fitted and cross-validated a classifier model, we interpret them as follows.

Logistic regression model. Logistic Regression models a relationship between an outcome variable

and a group of predictor variables in terms of log odds. In order to interpret the model, we compute the estimated weights for each feature and their corresponding odds ratio

(Molnar, 2019). If the odds ratio for a feature () is , it means that a unit increase in changes the odds of our outcome variable by a factor of when all other features remain the same. By calculating the features with the highest odds ratios for different labels, we are able to identify which features are the best predictors of dangerous/banned, hateful, and other/benign subreddits.

Decision tree and random forest models. Tree based models are the easiest to interpret. We find the importance of each feature using Gini Importance (Breiman, 2017). At a high-level the gini importance counts the number of times a feature is used as a splitting variable, in proportion with the fraction of samples it splits. For random forests, the gini importance is averaged over all the constructed trees. We expect more important features to have higher gini importance scores. Unlike logistic regression interpretation, a limitation here is that this metric only allows us to rank feature importance, but not quantify the relative difference of their importance. Further, we do not observe per-outcome importances.

4.2. Results

We now focus on measuring the effectiveness of using evolutionary features to predict which category (from dangerous/banned, hateful, and other) subreddits belong to (Section 4.2.1), which features are the best predictors of subreddit behavior (Section 4.2.2), and the impact of observation time on classifier accuracy (Section 4.2.3). Our results are summarized in Table 8.

Model Classifier Performance (%Accuracy, %F1) Top features
Q1 Q1 + Q2 Q1 + Q2 + Q3 Total
Logistic regression (74, 48) (77, 54) (78, 61) (84, 72) %connections to banned and hateful communities, #stickied posts and #moderators.
Decision tree (82, 70) (85, 74) (83, 69) (83, 71) #total connections to hateful and banned communities, distribution of post scores.
Random forest (89, 82) (90, 83) (90, 83) (90, 84) #total connections to hateful and banned communities, %connections to banned and hateful communities.
Table 8. Performance of our classifier models and the most important class of features used by the models. Q1 denotes that the classifier only had access to features from the first quarter of the subreddit lifespans, Q1+Q2 denotes access to the first half of the lifespan, Q1+Q2+Q3 denotes access to the first three-quarters, and Total denotes access to the entire lifespan.

4.2.1. Can we identify hateful or dangerous subreddits by their evolutionary features?

Column “Total” of Table 8 shows how our different explainable classifier models performed at classifying subreddits into banned (or, dangerous), hateful, and other when given access to all evolutionary features of the subreddit. As we can see all our models perform reasonably well achieving F1-scores from 71% (decision tree) to 84% (random forests) and accuracies of 83% (decision tree) to 90% (random forests) – compared to a baseline accuracy of 58% (for a classifier which simply outputs the most dominant label in our dataset – i.e., ‘other’). This promising result shows that evolutionary features can be used as a distinguisher between each class of subreddit. Further, in observing the confusion matrices obtained from our testing, we see that most classifiers had trouble distinguishing between subreddits with ‘dangerous/banned’ and ‘hate’ labels. In particular, several hate subreddits (e.g., r/The_Donald and r/inceltears) were consistently misclassified as dangerous/banned subreddits due to very similar evolutionary patterns. We note that since our classes rely on human coding (i.e., our banned/dangerous subreddits are essentially labeled by Reddit administrators), it is not clear if these misclassifications are a result of inadequate features or inconsistent application of content policies by Reddit administrators (although a cursory qualitative analysis of the content in r/The_Donald suggests the latter). The high accuracy of our models suggest that interpreting their use of evolutionary features will yield predictors of subreddit behaviors.

4.2.2. What features are most important for predicting the evolution into hateful or dangerous subreddits?

Column “Top features” of Table 8 shows the most important features for each of our classifier models using the interpretations described in Section 4.1.3. Here we see a common theme emerge: structural features capturing the connectivity of subreddit members to other hateful and dangerous/banned subreddits is the most discriminative predictor of subreddit behavior. This feature appears as our highest ranked feature for all classification models. Interpreting the logistic regression, we see the feature has an odds ratio of 3.96 – indicating very strong predictive value. Our findings here suggest that past participation of current community members in hateful and banned subreddits is the strongest predictor of future community behavior. Other features of importance include moderator team size and activity (measured by #stickied posts).

4.2.3. How far ahead of time can hateful or dangerous subreddits be predicted?

Our goal goes beyond identifying the labels associated with a subreddit. Since we are motivated by the need for tools to aid proactive (and early) community interventions, we also need to understand how early the future behavior of a subreddit can be predicted. To this end, we evaluated each classifier’s performance using features extracted only from the first quarter (Q1), first half (Q1+Q2), and first three-quarters (Q1+Q2+Q3) of a subreddit’s lifespan. For reference, subreddits in , which we label as dangerous, had the following lifespan distribution [: 7 months, percentile: 20 months, median: 34 months, percentile: 39 months, : 43 months]111These are lower bounds on lifespan of dangerous/banned subreddits since our data collection only started in 01/2015 and subreddits in might have been active before our data collection began.. From the results shown in Table 8, we see that while features from Q4 are the most important for the logistic regression and decision tree models, the random forest classifier has consistently strong predictive performance even in the first quarter of the subreddit’s lifespan. This shows that even features chosen from early in the subreddits lifetime (e.g., Q1) can be predictors of future subreddit behavior. This finding strongly reinforces the feasibility of early and pre-emptive interventions to correct subreddit behavior.

4.2.4. Takeaway: Can evolution into hateful or dangerous subreddits be predicted?

Taken together, our results show that evolutionary features can not only be used to classify current subreddit behavior, but also predict their future behavior and that features related to how the community is connected to other subreddits (i.e., network structural features) are the strong predictors. Therefore, we are able to confirm that evolution into hateful or dangerous subreddits can be predicted – i.e., hypothesis H2 is valid. This suggests that administrator and community moderation tools which rely on measuring the connectivity of subreddits to known hate or banned subreddits can be used to pre-emptively identify which subreddits require careful monitoring or even administrator/moderator interventions.

5. Impact of Moderation

So far our results have shown that topics and user bases associated with subreddits are, on average, evolving at a high rate (Section 3) and the characteristics of this evolution can be used to predict future subreddit behavior – particularly evolution into dangerous or hateful subreddits (Section 4). These findings suggest that community administration and moderation interventions can be used to pre-emptively mitigate the impact of such communities. In this section, we focus on understanding (1) the impact of no pre-emptive intervention on known dangerous and hateful communities and (2) the impact of interventions such as bans and quarantines on known dangerous and hateful communities. Specifically, we test the hypothesis: H3. (a) User participation in hateful or dangerous subreddits negatively changes the nature of their participation within the broader community and (b) this can be corrected by community bans and quarantines. These hypotheses, if valid, will show that community bans and quarantines as pre-emptive moderator actions can reduce offensiveness within the broader community. If H3(a) is invalid, we will have shown that user behavior in outside communities is not influenced by their participation in hateful or dangerous communities. If H3(b) is invalid, we will have shown that there is a need for more nuanced pre-emptive interventions to mitigate the impact of hateful and dangerous communities. Our methodological and result contributions are highlighted in Table 9.

Methodological questions Section 5.1
How do we quantify the nature of user participation? Section 5.1.1
How do we quantify the impact of events on user participation? Section 5.1.2
Research questions Section 5.2
What is the impact of joining a hateful or dangerous community on broader user behavior? Section 5.2.1
What is the impact of banning and quarantining hateful or dangerous communities on the behavior of their members? Section 5.2.2
Do ban and quarantine interventions improve user behavior? Section 5.2.3
Table 9. Hypothesis 3: (a) User participation in hateful or dangerous subreddits negatively changes the nature of their participation within the broader community and (b) this can be corrected by community bans and quarantines. Summary of methods and results.

High-level overview. At a high-level, our goal is to quantify the change in user behavior, in the broader community, that occurs as a result of three types of events: (1) joining a known hateful or dangerous subreddit, (2) the banning of a community they participate in, and (3) the quarantining of a community they participate in. To achieve this goal, we need to develop methods to (1) quantify user behavior and (2) measure the impact of each of the above events on these metrics. We accomplish this using the following approach, which is explained in more detail in Section 5.1. First, we quantify user behavior using two metrics – incidence of offensive comments and quantity of participation in known hateful and dangerous communities. Second, to understand the impact of certain events on their behavior, we carefully select treatment (users impacted by the event) and control (users similar to control group but not impacted by the event) groups and analyze how their behaviour varies before and after the event.

5.1. Methods

Our method for testing the proposed hypothesis requires two main methods. First, we need to develop metrics to quantify user behavior (Section 5.1.1). Next, we need to determine how to quantify the impact of an event on user behavior (Section 5.1.2).

5.1.1. How do we quantify the nature of user participation?

We quantify user behavior using two different metrics – (1) the incidence rate of offensive comments and (2) the fraction of their participation that occurs in communities known to be hateful or dangerous. In order to measure the incidence rate of offensive comments, we use an offensive speech classifier (Davidson et al., 2017) to detect offensive comments made by users. By running this classifier on each comment made by a user in a month, we are able to assign an incidence rate of offensiveness for each user for each month. In addition, for each user for each month, we also record the fraction of user activity (in terms of comments made) that occurred in dangerous () and hateful () communities.

5.1.2. How do we quantify the impact of events on user participation?

We analyze the impact of three types of events on a user’s behavior: (1) joining a known hateful or dangerous subreddit, (2) the banning of a community they participate in, and (3) the quarantining of a community they participate in. By tracking user behavior for the months before and after they were impacted by one of these events, we are able to see general trends which illustrate the impact of the event. We go one step further in our analysis of the impact of each events – we compare user behavior with a control group which is constructed as follows. For each user () in our treatment group (e.g., user who joined a known hateful or dangerous subreddit), we select the user () who was not impacted by the corresponding event (e.g., did not join the corresponding hateful subreddit) but shared the most similar community participation profile as . We add to our control group. By comparing the behavior of users in our control and treatment group, we can make a stronger case for causal relationships between user behavior and specific events.

5.2. Results

We now focus on using our described methods (Section 5.1) to understand the impact of community join (Section 5.2.1), ban and quarantine (Section 5.2.2) events on user behavior.

5.2.1. What is the impact of joining a hateful or dangerous community on broader user behavior?

Figure 3 shows the impact of joining dangerous () and hateful () communities on user behavior. We can make several interesting observations from these results.

Joining hateful communities increases offensiveness in the broader community. Looking at Figure 2(a) we see that during the month of the join event associated with hate subreddits, our treatment users show an increase in offensive behavior. Interestingly, we see that our treatment users who joined dangerous subreddits show signs of increased offensiveness from our control group for many months before the join event (Figure 2(c)). Seeking to better understand this trend leads us to our next observation.

Hateful communities are a pipeline to dangerous communities. Looking at Figure 2(b), we see that joining a hate community leads to nearly 500% more activity in dangerous communities. Alarmingly, this increase happens within the same month of the join event and remains high for many months after. This is strong evidence of a causal relationship between joining hate communities and participation in dangerous communities. We do not see a similar trend when analyzing the rate of participation in hateful communities after joining a dangerous community (Figure 2(d)).

(a) Metric: Offensiveness rate. Event: Joining hateful subreddit ().
(b) Metric: Rate of participation in dangerous communities (). Event: Joining hateful subreddit ().
(c) Metric: Offensiveness rate. Event: Joining dangerous subreddit ().
(d) Metric: Rate of participation in hateful communities (). Event: Joining dangerous subreddit ().
Figure 3. The impact of joining events on user behavior. Month “0” represents the month of the join event.

5.2.2. What is the impact of banning and quarantining hateful or dangerous communities on the behavior of their members?

Figure 4 shows the impact of banning or quarantining communities on the behavior of their members. We make the following observations.

Bans and quarantines of dangerous communities do not reduce offensiveness of the impacted members. Looking at Figure 3(a) and Figure 3(c) we can see that there is no significant difference in incidence rates of offensive comments before and after a ban or quarantine event for our treatment users.

Impacted community members of bans and quarantines simply move to other hate subreddits. We see from Figure 3(b) that users impacted by community bans continue a high level of engagement in other hate communities. Surprisingly, our data shows (Figure 3(d)) that when communities get quarantined, their users typically increase their participation in hate communities significantly – after a lag of several months (and possibly after the quarantined community was banned).

(a) Metric: Offensiveness rate. Event: Banning dangerous subreddit ().
(b) Metric: Rate of participation in hate communities (). Event: Banning dangerous subreddit ().
(c) Metric: Offensiveness rate. Event: Quarantining dangerous subreddit ().
(d) Metric: Rate of participation in hateful communities (). Event: Quarantining dangerous subreddit ().
Figure 4. The impact of ban and quarantine events on user behavior. Month “0” represents the month of the event.

5.2.3. Takeaways: Do ban and quarantine interventions result in improved user behavior?

Taken together, our analysis shows that participation in dangerous () and hateful () subreddits does have an impact on a users behavior in the wider community and current administration strategies of bans and quarantines are not effective for mitigating the impact of this participation on individual users. Through our results, we are able to: (1) confirm hypothesis (H3a) – i.e., user participation in hateful subreddits negatively changes the nature of their participation in the broader community and (2) reject hypothesis (H3b) – i.e., we see that banning or quarantining subreddits does not result in improved user behavior. This suggests the need for more nuanced interventions to mitigate the impact of hateful and dangerous communities.

6. Related Work

At a high-level, in this work, we make contributions in three dimensions: First, we perform measurements to understand how topics and user bases of online communities change over time (Section 3). Second, we identify the predictors of “negative evolution” of a community – i.e., evolution into an uncivil or offensive community (Section 4). Finally, we conduct large scale measurements to understand how user behavior can be influenced by moderator actions which enable or disable participation within toxic communities (Section 5). In this section, we break down the related work in each of these dimensions. Specifically, in Section 6.1 we explore related research seeking to understand how communities evolve and in Section 6.2 we explore research aimed at measuring and mitigating incivility in online communities.

6.1. Evolution of online communities

Participation in online communities is increasingly common and studying behavioral patterns and evolution in these communities has been the subject of several research efforts. These efforts can be taxonomized by whether the goal is to understand evolution of interaction quantity or quality. Below, we present (a subset) of related work in these categories.

Interaction quantity. Research in this category has generally focused on understanding how the amount of interaction occurring in a community changes over time and under different conditions. A general approach is to model community interactions as a network graph where edges denote interactions (e.g., messages sent between two users) between nodes (i.e., community members) and track their evolution under different conditions. Especially relevant to our work is research from Crandall et al. (Crandall et al., 2008) which among other results showed that interaction network related features are predictive of future user behavior in topic-centered communities. Other research focused exclusively on understanding (rather than predicting) user interaction evolution in different communities. Kumar et al. (Kumar et al., 2010) studied the Flickr and Yahoo! 360 communities to understand the role of specific users in community growth. They found that a small number of key users are responsible for expanding a community and in the absence of these groups community growth stagnated. Ngamkajornwiwat et al. (Ngamkajornwiwat et al., 2008) focus on understanding how the network structure of online open source software development communities evolve over time. They found that relationships between “core” and “peripheral” developers generally weakened over time and contributions from the peripheral members tended to decrease. These results generally highlight the influence of a few community members on the direction and trends observed in a community.

Interaction quality. Research in this category has generally focused on understanding how the nature of interactions (i.e., its qualities) within a community change over time and under different conditions. Garcia et al. (Garcia et al., 2013), in a post-mortem of Friendster, showed that it is insufficient to consider only interaction network related features when modeling a community’s resilience to decline specifically highlighting the need to consider qualitative and external features. The importance of external events is also highlighted by Zannettou et al. (Zannettou et al., 2018, 2017) who focused on the evolution of memes and news sources within communities and uncovered their influence on external communities. Focusing exclusively on Reddit, Mills et al. (Mills, 2018; Mills and Fish, 2015) showed that, for r/The_Donald and r/Sanders4President, external events and their community participation guidelines were largely responsible for their rise in popularity and large influx of users. These studies highlight the need to consider cross-community interactions and external events when considering evolution of communities. Several studies have also investigated how specific user interactions are influenced by the age of a community. Danescu et al. (Danescu-Niculescu-Mizil et al., 2013) found that linguistic features in a community were constantly evolving and found that its newest members were most likely to adapt their own linguistic features to those of the community. Gazan (Gazan, 2009) found that, when communities stabilized, topics tended to move away from topical and factual to personal and social. This generally resulted in increased participation, often at the cost of conflict and factionalism. Rather surprisingly, Kiene et al. (Kiene et al., 2016) showed that after a certain point in the life-cycle of a community, large influxes of users had no impact on the quality of discourse within the subreddit. These studies highlight the need to consider age and stability of a community when predicting its evolution. Focusing on the impact of status in online communities, Bhalla et al. (Bhalla and Lampel, 2007) found that, over time, qualitative characteristics of a community began matching the characteristics of the elite. Along similar lines, Gervais (Gervais, 2014) and Kwon et al. (Kwon and Gruzd, 2017) showed that exposure to incivility from political elites results in more offensive rhetoric in online communities. In terms of methods, we find most similarity between our approach and the work of Matias (Matias, 2016a, b) which used a logistic regression model to attribute weights to survey-derived features to uncover the factors associated with moderators and subreddits participating in the Reddit-wide blackout of 2015 – in protest of Reddit’s administrative actions. They uncovered a strong correlation between moderator participation in meta-reddit subreddits and community participation in the protest. These findings further highlight the important role played by a few key members (elites and moderators) in a community.

6.2. Incivility in online communities

Online communities have become an increasingly popular medium for different types of discourse. This is particularly true for discourse around controversial sociopolitical topics due to the pseudonomity provided by the Internet and the resulting disinhibition effect (Suler, 2004). Analyzing this discourse has been the subject of much research, with many focusing on “incivility” which is defined as “communication that violates the norms of offensiveness” (Mutz, 2015). These research efforts can be taxonomized by whether the goal is to measure the incidence rates and propagation of incivility or to identify and evaluate strategies for mitigating the impact of such behaviors. Below, we highlight (a subset) of related work in these categories.

Measuring incivility. Research in this category has generally focused on understanding the incidence rates and propagation of online offensive discourse in different contexts. Olteanu et al. (Olteanu et al., 2018) found that offensive speech on Reddit and Twitter were correlated with the occurrence of violent offline events. Along similar lines, Nithyanand et al. (Nithyanand et al., 2017b, a) found that external political events and a change in media consumption habits in online communities were correlated with a rapid increase of online incivility in political subreddits. In addition, they traced the movement of offensive authors across communities highlighting the sources and sinks of offensiveness on Reddit.

Mitigating incivility. Research in this category has generally focused on understanding the impact of different types of interventions on uncivil behaviour. Massanari (Massanari, 2017) conducted a qualitative analysis of the Reddit communities at the center of the Fappening (Kosur, 2015; Fap, 2014) and Gamergate controversies (Tsukayama, 2014; Parkin, 2014). The study highlights how the inaction of Reddit administrators and community moderators resulted in the emergence of toxic technocultures and argues for the exploration of alternative designs and moderation tools to combat the spread of such toxicity. This study is complemented by Lapidot et al. (Lapidot-Lefler and Barak, 2012) which demonstrated that, given anonymity, there was a strong positive correlation between supervision and toxic inhibition. These studies demonstrate that the absence of interventions (i.e., moderation) is correlated with incivility. Birman (Birman, 2018) and Fiesler et al. (Fiesler et al., 2018) conduct measurements and characterize the different (explicitly stated) moderation strategies employed by different communities on Reddit. Complementing these works, Eshwar et al. (Chandrasekharan et al., 2018) study moderator-removed comments and use this information to understand the (implicit) moderation strategies and subreddit rules. Along similar lines, Kiene et al. (Kiene et al., 2016) analyzed moderator behavior during large influxes of users into a community to uncover strategies for maintaining content quality and civility. In terms of methods, we find most similarity between our work and research from Chandrasekharan et al. (Chandrasekharan et al., 2017). They authors focus on uncovering the effectiveness of two subreddit bans: r/FatPeopleHate and r/CoonTown by tracking the behaviour of two sets of users – a treatment set (who visited the banned subreddits) and a control set (who shared identical subreddit memberships as the treatment, except for presence in the banned subreddits) before and after the bans. The study concluded that the bans resulted in a general reduction of offensiveness in all interactions for members belonging in the treatment group. In our work, we follow an identical approach only differing in the scale and points of interest. First, our study considers over 3K subreddits including 100’s of offensive, banned, and quarantined subreddits. Second, we are interested in analyzing how user behaviour changes as a consequence of two events: (1) when they join a community and (2) when the community is banned or quarantined. Taken together, these studies enumerate the different moderation strategies used by online communities and methods for assessing their effectiveness.

7. Conclusions

In this paper, we conducted a comprehensive study on the feasibility of proactive moderation strategies for Reddit communities. Specifically, we showed that Reddit communities, on average, have a high rate of evolution and this requires constant monitoring, from moderators and administrators, for hateful and dangerous communities – a prohibitively expensive proposition (Section 3). However, our observation that the evolutionary patterns for hateful communities (i.e., those frequently reported for hate speech) and dangerous communities (i.e., those which were eventually banned by Reddit for violating the content policy) are different from other communities yields an opportunity for easing moderator effort. We harness this insight to build simple and explainable machine learning models which are able to study evolutionary characteristics of subreddits and predict their future behavior with reasonably high accuracy. Each of our classifiers show that structural properties of the community (i.e., which other communities its users interact with) are the strongest predictors of future community behavior Section 4. This finding suggests that tools which capture the connectivity of subreddits to known hate or banned subreddits can be used to identify which subreddits require careful monitoring or pre-emptive interventions. Finally, we consider the impact of different events on user behavior and show that joining hateful subreddits significantly worsens user civility even in the broader context. Also, worryingly we find that current methods for community-level interventions (i.e., bans and quarantines) do not have an impact on the civility of their impacted users (section 5). This suggests that there is a need for the development of more active and nuanced intervention strategies to effectively moderate hateful and dangerous communities. While our results are largely focused on Reddit, one of the largest and most controversial online communities, our findings can be useful for administrators and moderators of any online community considering proactive moderation to prevent the growth of hateful and dangerous communities.

References