Towards Understanding the Information Ecosystem Through the Lens of Multiple Web Communities
arXiv:1911.10517
Abstract
The Web consists of numerous Web communities, news sources, and services, which are often exploited by various entities for the dissemination of false information. Yet, we lack tools and techniques to effectively track the propagation of information across the multiple diverse communities, and to model the interplay and influence between them. Also, we lack an understanding of what the role and impact of emerging communities and services on the Web are, and how such communities are exploited by bad actors that spread false and weaponized information. In this thesis, we study the information ecosystem on the Web by presenting a typology that includes the various types of false information, the involved actors and their possible motives. Then, we follow a data-driven cross-platform quantitative approach to analyze billions of posts from Twitter, Reddit, 4chan's /pol/, and Gab, to shed light on: 1) how news and memes travel from one Web community to another and how we can model and quantify the influence between Web communities; 2) characterizing the role of emerging Web communities and services on the Web, by studying Gab and two Web archiving services, namely the Wayback Machine and archive.is; and 3) how popular Web communities are exploited by state-sponsored actors for the purpose of spreading disinformation. Our analysis reveal that fringe Web communities like 4chan's /pol/ and The_Donald subreddit have a disproportionate influence on mainstream communities like Twitter with regard to the dissemination of news and memes. We find that Gab acts as the new hub for the alt-right community, while for Web archiving services we find that they can be misused to penalize ad revenue from news sources with conflicting ideology. Finally, when studying state-sponsored actors, we find that they were particularly influential in spreading news on popular communities like Twitter and Reddit.
arXiv commentPhD thesis. Overlaps with arXiv:1804.03461, arXiv:1705.06947, arXiv:1805.12512, arXiv:1802.05287, arXiv:1801.10396, arXiv:1801.09288, arXiv:1811.03130
References in corpus (30)
- Fast unfolding of communities in large networks
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- Distributed Representations of Sentences and Documents
- Optimal detection of changepoints with a linear computational cost
- Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads
- Science vs Conspiracy: collective narratives in the age of (mis)information
- Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election
- The power of a good idea: quantitative modeling of the spread of ideas from epidemiological models
- Hoaxy: A Platform for Tracking Online Misinformation
- Emotional Dynamics in the Age of Misinformation
- Online Human-Bot Interactions: Detection, Estimation, and Characterization
- False Information on Web and Social Media: A Survey
- Fake News Mitigation via Point Process Based Intervention
- Social Politics: Agenda Setting and Political Communication on Social Media
- Determining the Veracity of Rumours on Twitter
- A Stylometric Inquiry into Hyperpartisan and Fake News
- Adscape: Harvesting and Analyzing Online Display Ads
- Scalable Bayesian Inference for Excitatory Point Process Networks
- Spotting Rumors via Novelty Detection
- Debunking in a World of Tribes
- Viral Misinformation: The Role of Homophily and Polarization
- Investigating Rumor Propagation with TwitterTrails
- Hate is not Binary: Studying Abusive Behavior of #GamerGate on Twitter
- Image Credibility Analysis with Effective Domain Transferred Deep Networks
- Homo Cyberneticus: The Era of Human-AI Integration
- Cost Effective Rumor Containment in Social Networks
- We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!
- An Efficient Randomized Algorithm for Rumor Blocking in Online Social Networks
- It's Always April Fools' Day! On the Difficulty of Social Network Misinformation Classification via Propagation Features
- Detection and Analysis of 2016 US Presidential Election Related Rumors on Twitter