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  • Influence
Political Polarization on Twitter
TLDR
It is demonstrated that the network of political retweets exhibits a highly segregated partisan structure, with extremely limited connectivity between left- and right-leaning users, and surprisingly this is not the case for the user-to-user mention network, which is dominated by a single politically heterogeneous cluster of users.
BotOrNot: A System to Evaluate Social Bots
TLDR
BotOrNot, a publicly-available service that leverages more than one thousand features to evaluate the extent to which a Twitter account exhibits similarity to the known characteristics of social bots, is presented.
The rise of social bots
Today's social bots are sophisticated and sometimes menacing. Indeed, their presence can endanger online ecosystems as well as our society.
Online Human-Bot Interactions: Detection, Estimation, and Characterization
TLDR
This work presents a framework to detect social bots on Twitter, and describes several subclasses of accounts, including spammers, self promoters, and accounts that post content from connected applications.
Predicting the Political Alignment of Twitter Users
TLDR
Several methods for predicting the political alignment of Twitter users based on the content and structure of their political communication in the run-up to the 2010 U.S. midterm elections are described and a practical application of this machinery to web-based political advertising is outlined.
The science of fake news
TLDR
Social and computer science research regarding belief in fake news and the mechanisms by which it spreads is discussed, focusing on unanswered scientific questions raised by the proliferation of its most recent, politically oriented incarnation.
The spread of low-credibility content by social bots
TLDR
It is found that bots play a major role in the spread of low-credibility content on Twitter, and control measures for limiting thespread of misinformation are suggested.
Detecting and Tracking Political Abuse in Social Media
TLDR
A machine learning framework that combines topological, content-based and crowdsourced features of information diffusion networks on Twitter to detect the early stages of viral spreading of political misinformation.
Social phishing
Sometimes a "friendly" email message tempts recipients to reveal more online than they otherwise would, playing right into the sender's hand.
Virality Prediction and Community Structure in Social Networks
TLDR
It is demonstrated that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration, and the more communities a meme permeates, the more viral it is.
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