A Pipeline for Graph-Based Monitoring of the Changes in the Information Space of Russian Social Media during the Lockdown

by   V. Danilova, et al.

With the COVID-19 outbreak and the subsequent lockdown, social media became a vital communication tool. The sudden outburst of online activity influenced information spread and consumption patterns. It increases the relevance of studying the dynamics of social networks and developing data processing pipelines that allow a comprehensive analysis of social media data in the temporal dimension. This paper scopes the weekly dynamics of the information space represented by Russian social media (Twitter and Livejournal) during a critical period (massive COVID-19 outbreak and first governmental measures). The approach is twofold: a) build the time series of topic similarity indicators by identifying COVID-related topics in each week and measuring user contribution to the topic space, and b) cluster user activity and display user-topic relationships on graphs in a dashboard application. The paper describes the development of the pipeline, explains the choices made and provides a case study of the adaptation to virus control measures. The results confirm that social processes and behaviour in response to pandemic-triggered changes can be successfully traced in social media. Moreover, the adaptation trends revealed by psychological and sociological studies are reflected in our data and can be explored using the proposed method.



There are no comments yet.


page 1

page 23

page 26

page 27


Understanding the Spatio-temporal Topic Dynamics of Covid-19 using Nonnegative Tensor Factorization: A Case Study

Social media platforms facilitate mankind a data-driven world by enablin...

COVID-19 on Social Media: Analyzing Misinformation in Twitter Conversations

The ongoing Coronavirus Disease (COVID-19) pandemic highlights the inter...

Image-based Social Sensing: Combining AI and the Crowd to Mine Policy-Adherence Indicators from Twitter

Social Media provides a trove of information that, if aggregated and ana...

Graph-Hist: Graph Classification from Latent Feature Histograms With Application to Bot Detection

Neural networks are increasingly used for graph classification in a vari...

Addressing machine learning concept drift reveals declining vaccine sentiment during the COVID-19 pandemic

Social media analysis has become a common approach to assess public opin...

Artificial Intelligence for Emotion-Semantic Trending and People Emotion Detection During COVID-19 Social Isolation

Taking advantage of social media platforms, such as Twitter, this paper ...

Measuring Linguistic Diversity During COVID-19

Computational measures of linguistic diversity help us understand the li...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.