Correlated Feature Selection for Tweet Spam Classification using Artificial Neural Networks

11/06/2019 ∙ by Prakamya Mishra, et al. ∙ 0

Identification of spam messages is a very challenging task for social networks due to its large size and complex nature. The purpose of this paper is to undertake the analysis of spamming on Twitter. To classify spams efficiently it is necessary to first understand the features of the spam tweets as well as identify attributes of the spammer. We extract both tweet based features and user based features for our analysis and observe the correlation between these features. This step is necessary as we can reduce the training time if we combine the features that are highly correlated. To perform our analysis we use artificial neural networks and train the model to classify the tweets as spam or non-spam. Using Correlational Artificial Neural Network gives us the highest accuracy of 97.57% when compared with four other classifiers SVM, Kernel SVM, K Nearest Neighbours and Artificial Neural Network.



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1 Introduction

Online social networking platforms such as Twitter, Facebook, Instagram etc. allow people to meet, discuss and work together by collaborating on projects with just a click. The combined number of users just on Facebook, Linkedin, Instagram and Twitter stands at 3200 million as of September 2017 111 Twitter has generated a lot of interest among netizens recently due to its widespread use by influential people like the Presidents and the Prime Ministers of powerful countries. As per latest reports, approximately 330 million active users are on twitter .
One of the interesting properties of Twitter is the ability to follow any other user with a public profile. Media organisations, politicians, and celebrities are reaching millions of followers everyday. It is interesting to note that in many cases, the number of actual followers is not genuine [3]. In a recent incidence in India, some of the leading national newspapers published a headline news on Oct 21, 2017 with the title being ”Bots behind rise in XXXX Twitter popularity?” when one of his tweets received 30,000 re-tweets.
Fake Twitter followers and Robot driven accounts being used for re-tweets is not a new phenomena. There are softwares in the market such as Twitter bots that use Twitter APIs to control Twitter accounts. These software bots can be used to send tweets, re-tweets, follow, unfollow and increase the number of likes on a tweet [10]. Varol et al. [11] in his paper states that as many as 48 million accounts in Twitter are actually bots. This means that approximately 15% of the profiles are fake. The growth in popularity of Twitter in the recent years has led to a large number of spammers who exploit and manipulate these numbers to misuse the whole medium for unwanted gains.
Spamming is not a new concept. Initially the term was associated with bulk email which were unsolicited. Lots of research has been done to overcome this problem and now we have fairly accurate filters which keep on segregating spam mails into a separate folder.

The internet society in 2015 estimated that 85% of the global emails are spam

222 Considering that decades of research has been done in the area of email filtering, Tweet spam filtering needs significant contribution from the academic research community.
Twitter defines spam as unsolicited, repeated actions that negatively impact other people. Examples of the afore mentioned could be, posting harmful links of phishing sites, using automated bots for mass following, abusing, creating multiple handles, posting or re-tweeting on trending topics unnecessarily .
Twitter has it’s own mechanism of spam detection however it is still in its infancy and the academic research community should provide it’s support. On an average Twitter is able to detect roughly 3.2 million suspicious accounts per week 333 The primary issue that needs attention is to understand how harmful these spams can be. To figure out a solution to this problem, we note that the US Intelligence community released a report in January 2017 highlighting the role that Russia Today (RT) might have played in influence the 2016 U.S. Elections. This is a big allegation and which is still under investigation; however, it still shares the influence of online platforms particularly Twitter for such big events [2] .

In section 2, we will review related spam detection research. In section 3, we will explain our process of data collection and preprocessing. In section 4, we will discuss our approach for feature selection which further divided into 2 sub sections for the analysis of Tweet based features and User based features. Section 5, we detail our experiments and comparisons with our spam detection model. Section 6 concludes this research with directions to future work.

2 Related work

Twitter continues to gain popularity among the various social networks currently available and thus attracts spammers who would try to abuse the system by manipulating existing features to gain undue advantage [10].

There have been numerous studies which have proposed machine learning and artificial intelligence techniques for detecting spammers. Existing studies have focussed on classification algorithms to distinguish between spammers and non-spammers

[3]. Lee et al. [7] created social honeypots for the identification of spammers. Many studies have focused on URL based spam detection and blacklists based on domain and IP address. This has not been successful since short URL’s obscure the base and new short URL’s are used by spammers as soon as old ones are blacklisted [13]. Grier et al. worked extensively on blacklisted URL’s [4].
Twitter identifies users through a unique username referred to as the screen name. Each user can send replies containing screen names. One can also mention another user’s screen name anywhere in their tweet. This feature helps users to track conversations and know each other. Spammers, however use this feature by including many screen names in their replies and tweets, If there are too many replies or mentions in tweets by an user, Twitter will treat this as suspicious444Twitter support [8].
Twitter allows a message with a maximum length of 140 characters. Due to this restriction many URLs are shortened in the tweets. However, short URLs can obscure the source and this property has been used by spammers to camouflage the spam URLs [13]. Twitter allows the re-tweets and all such re-tweets start with @ RT. Many authors [5] use the number of re-tweets in the most recent 20-100 tweets of a user as important feature in spam detection.
Trending topic has become a ubiquitous topic these days. If there are several tweets with the same term, then it will become a trending topic. Spammers seek attention by posting many unrelated tweets with trending terms [12].
Another prominent feature of Twitter allows users to create public and private list to categorize people in different groups based on similar interests [6] . There are two major categories in which we can segregate the extracted features for Twitter spam detection. User profile based features and tweet content based features. Some of the major user profile based features are the number of followers, the number of follows, duration of the existing account, the number of favourites, number of lists in which the user has membership and the average number of tweets a particular user sends [5]. The tweet content based features are the number of times a particular tweet has been re-tweeted, the number of hashtags, the number of times a particular tweet has been mentioned, the number of URLs included in a tweet, the number of characters and number of likes in this tweet [6].

3 Data collection

We need labelled dataset to train and test our model. A majority of spam contains embedded URLs and many researchers have focused only on this [9]. For making our labelled dataset of spam and non-spam tweets, we have used tweets from spammers and non-spammers. We have used the list of spammers from the reference [1] and for non-spammers we have randomly picked Twitter user accounts. For each user we have extracted at most 100 previous tweets using Twitter API Tweepy by giving user screen name as a query. Along with the text of the tweets we have also extracted timing of the tweets, number of previous tweets, favourites, friends, lists, number of followers of the users.
We have extracted 719300 tweets from a of total 760 users out of which 370 (350900 tweets) users are randomly picked non-spammers and 390 (368400 tweets) are spammers.

4 Feature selection

Any machine learning based spam classification technique would need feature extraction. Historical information of user based features such as number of tweets sent by user in last 30 days etc. are important and give useful insights. To make sure that feature extraction is a real time process, we have used light weight features from Tweepy API and derived new features from these extracted features.

One of the important difference between spammers and non-spammers is the intent of spamming. Spammer’s purpose of tweeting is to get some undue advantage through those tweets or belittle some rival. Taking into account the intentions of the tweets it is obvious that the spammer’s tweets should have distinct characteristics compared to non-spammer’s tweets. Historical data suggests that the average time spent by non-spammers should be less than spammers [14]. There can be various other discriminatory attributes which can reflect on user behaviours.
In this section we will take into account the features that have been considered for each tweet for the purpose of classification. Each tweet has two broad categories of features viz. tweet based features, such as those which are related to that particular tweet like upper-case percentage in the tweet or time of tweet posted etc, and user based features, such as number of follower/following of the user etc. From the Tweepy API we have already extracted past tweets of a user from a list of 76 pre labelled spammers and non-spammers.

Tweet based features and user based features together comprises of 21 features that are tweet, tweet time, number of followers, number of tweets, number of favourites, number of friends, number of lists, number of screen names used, upper-case percentage use, number of link/URLs used, link to word percentage, same screen name percentage use, tweet similarity percentage, user re-tweet percentage, user average screen name usage, user tweet frequency, user previously re-tweeted or not, user upper-case percentage, user average link/URLs count, user link to word percentage, user link use frequency, user same screen name percentage use, user Tweet length standard deviation, user tweet similarity.

4.1 Tweet based features

From only the text of the tweet, we can get a lot of properties like upper-case percentage, number of screen names in a tweet, link to word percentage, same screen name percentage and tweet similarity. These features correspond to the textual attributes of the tweet which are useful for spam classification.
The Tweet similarity percentage Ts is calculated as shown in equation (1) ,


Let the tweet of which we are finding the tweet similarity percentage be T. We compare T having nt words with all the previous tweets Ti’s of that user and each comparison is given a percentage of similarity PSi which is percentage of number of similar words ns which are there in both the tweets except all the hashtags, screen names and links to nt and the average of all these PSi’s is calculated to give the tweet similarity percentage of the tweet T. This gives how much this tweet is similar to the previous tweets of the user that has posted this tweet.

Figure 1: Tweet similarity % - tweet

From Fig.1 it can easily be seen that spammers post mostly similar tweets but the non-spammers do not. From the dataset it can be seen that spammers only post similar tweets with same agenda every time i.e. to promote a link or user or advertise some of their product.
It is observed from our dataset that most of the spam tweets have a high percentage use of upper-case characters, for example tweets like ”PSP DOWNLOAD Music and Movies HERE! Have the BEST Experience EVER!”. Upper-case in tweets is usually used to emphasise some part of the tweet. Upper-case percentage U can be found by equation (3) where nu is number of upper-case words in the tweet and nt is total number of words in the tweet.


Figure 2: Uppercase % - Tweet

From Fig.2 we can easily identify that most of the spammers use high percentage of upper-case words in there tweets but non-spammers don’t.
There are some spammers who have a tendency to act as follow train. Follow trains are those types of Twitter accounts which advertise different user accounts by posting a lot of similar kind of tweets which includes requests to follow random users who have started following the follow train. There strategy is to attract different users to follow them by giving them the attractions of gaining followers and then they advertise their products. So number of screen names in a tweet plays an important role for classification of these types of spammers.

Figure 3: Screen name use count - tweet

From Fig.3 we can directly see that most of the tweets from the users use high number of screen names and the reason observed from the dataset is that they have a high tendency of to promote other users but this is not the case with non-spammers.

Figure 4: Link to word % - Tweet

There are some spammers which have a natural tendency to advertise themselves by providing links to their products in the tweets. Link to word percentage L2W can be found by equation (4) where nl is number of links in the tweet and nt is total number of words in the tweet.


It is observed from Fig.4 that spammers usually have low Link to word percentage and the reason for this which is observed from the dataset is that most spammers post tweets which mostly have less words and more link.

4.2 User based features

Text of the tweet alone cannot be used for spam classification. From the dataset which we have made, we can see that users who post spam tweets also have different trends in some of the user based properties like re-tweet percentage, link use percentage, percentage of time user tweeted with same screen name, tweet frequency, upper-case use percentage, standard deviation of tweet length, tweet similarity percentage of the user, number of followers and following, number of tweets, number of lists, number of favourites.
As explained above, tweet similarity percentage plays an important role for spam classification purpose. From Fig.5 it can easily be seen that spammers post mostly similar tweets but the non-spammers don’t. From the dataset it can be seen that spammers only post similar tweets with same agenda every time i.e. to promote a link or user or advertise some of their product. User’s tweet similarity percentage can be calculating by taking average of Ts’s of all the tweets of an user where Ts is calculated by equation (1)

Figure 5: Tweet similarity % - user

A re-tweet is a re-posting of a tweet. Twitter’s re-tweet feature helps to quickly share that tweet with all of the followers. One can re-tweet their own tweets or tweets from other users. Sometimes people type ”RT” at the beginning of a Tweet to indicate that they are re-posting someone else’s content. It is observed from our dataset that most of the non-spam users have a tendency to re-tweet other posts or there own posts, but in the case of spam users they normally do not post re-tweets however if some of the spammers do re-tweeting then the same tweet is used again and again.

Figure 6: Retweet % - user

Figure 7: Retweeted before - user

It is observed from Fig.6 and Fig.7 that most of the users who are spammers never post re-tweets or if they do then they only post re-tweets of similar other spam accounts only , but on the other hand most of the non-spam users frequently posts re-tweets.

Figure 8: Link use frequency % - user

Figure 9: Avg link per tweet - user

Link/URL use frequency percentage L, is also an important feature for classification of those type of spammers who usually send a high percentage of tweets having links with high frequency. These type of spammers usually post tweets with similar links which are associated with some product they want to advertise about. It can be calculated by equation (5) where nl is number of previous tweets of the users with any links/URLs and nt is total number of tweets of the users.


From Fig.8 and Fig.9 we can see that spammers either have very high frequency of tweets with links or very low but that’s not the case with non-spammers, It is also observed that those spammers which have a high frequency of link use mostly the same set or same links in each of their post which they want to advertise or if they have a very low link use frequency then they are not advertising any link but it is observed that they mostly advertise other user and most of their posts are related to increasing the follow count of other users.

Figure 10: % time user tweeted with same screen name

Those type of spammers who have a tendency to advertise other users and get there following to advertise their product uses a lot of screen names in there tweets. So percentage of time user tweeted with same screen name S is calculated by finding the percentage of number of all the unique screen names nus used to all the number of all screen names nts used in all the tweets of the user as shown in equation (6).


From Fig.10 it is observed that normal users mostly use same screen name in there tweets but spammers either use no screen name, from the dataset it is observed that these are those types of spammers who only promote links in there posts but not advertise users. Other type of spammers are those that always use same screen name in their posts and always want to promote same set of user in there tweets. Rest of the spammer don’t use same screen name frequently (They advertise many different users In there tweets).
From Tweepy Twitter API we can also extract the time at which a given tweet was posted. We have used this feature to find tweet frequency of user.

Figure 11: Tweet frequency

So from Fig.11 we can easily observe that mostly all the spammers have high tweet frequency of tweet and on the other hand normal users have very low tweet frequency. These type of spammers are usually some type of bots. These type of bots have a tendency to post similar type of tweets.
Tweet length is also an important factor. It is observed from the dataset which we have generated that usually there is a lot of variation in length of the tweet of normal user but the length of the tweet of the spammers usually remains the same. So we have also considered standard deviation of tweet length as an important user based feature.

Figure 12: Standard deviation of tweet length

From Fig.12 we can see that most of the spammers have low standard deviation in tweet lengths which signifies that most of there tweets are of same length and then from observing the dataset we can see that most of them have same length and format but there is only minor change like change in the link in the tweet or change in the screen name but this is not the case with normal users.
As we have discussed above that the upper-case use in the tweets is used for emphasising certain information within the tweets. This is heavily used by the spam users to advertise. So we calculate this by finding the percentage of time user uses upper-case in there tweets.

Figure 13: Uppercase % - user

From Fig. 13 we can easily identify that most of the spammers use high percentage of upper-case words in their tweets but normal users don’t.
Other properties considered in this paper are tweet similarity percentage, number of followers and following, number of tweets, number of lists, number of favourites.

5 Experiment and evaluation

For our experiment we have used Pearson correlation coefficient for finding correlation between different features 555 In statistics, the Pearson correlation coefficient is a measure of the linear correlation between two variables X and Y. It has a value between +1 and 1, where 1 is total positive linear correlation, 0 is no linear correlation, and 1 is total negative linear correlation. It is calculated as shown in equation (7).

Figure 14: Correlated Features

cov(x,y) is the covariance
x is the standard deviation of x
y is the standard deviation of y

For evaluation of the classifiers we have used 4 metrics that are precision, recall, F1 score and accuracy. Precision and recall are a useful measure of success of prediction when the classes are very imbalanced. In information retrieval, precision is a measure of result relevancy, while recall is a measure of how many truly relevant results are returned.Precision (

P) is defined as the number of true positives (Tp) over the number of true positives plus the number of false positives (Fp).


Recall (R) is defined as the number of true positives (Tp) over the number of true positives plus the number of false negatives (Fn).


These quantities are also related to the F1

score, which is defined as the harmonic mean of precision and recall.


From all the above plots we can see that there are a lot of correlated features so to find the correlation between all 21 different features ,we had made a matrix of all the features and found the Pearson’s correlation value between different features. From these Pearson’s correlation coefficient values we have combined the most correlated features by taking their products. There were 11 sets of correlated features as shown in Table 14. In our model we have used these 11 correlated features and passed it through an artificial neural network having 11 input units, 6 hidden units and one output unit. AUC of correlational ANN is shown in Fig.15

Model Name Precision Recall F1 Score Accuracy
Correlational ANN 0.9889 0.9557 0.972 97.5717
SVM 0.9115 0.9884 0.9484 95.1682
KSVM 0.9705 0.8867 0.9267 93.7014
KNN 0.9411 0.952 0.9465 95.1682
ANN 0.9795 0.9611 0.9702 96.9423
Table 1: Result.

Figure 15: AUC of correlational ANN

6 Conclusion

We identified a total of 21 features related to a tweet that contained attributes from both the tweet and the user the corresponding user. While computing the correlation between the features, it was observed that all the features could be grouped into 11 sets of correlated features. Thus our input for the artificial neural network gets reduced to 11 nodes. We apply the ANN for classification on data collected from Twitter API where, we used 80% for training and 20% for test. This classifier showed better performance than the four other classifiers that we compared with namely SVM, Kernel SVM, K Nearest Neighbours and Artificial Neural Network . On testing it was observed that precision, F1 score and accuracy improved for the same dataset with Correlational Artificial Neural Network as shown in Table 1. While we saw a slight decrease in the recall value. However this can be addressed as a future work where we not only look into features of individual tweets but also look at the links and identify patterns between the tweets and the users. It would be interesting to study if this additional information would improve our results further.


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