Matrix Factorization Techniques for Recommender Systems

What is Twitter

The spread of fake news on social media became a public concern in the United States after the 2016 presidential election. We examined exposure to and sharing of fake news by registered voters on Twitter and found that engagement with fake news sources was extremely concentrated. Only 1% of individuals accounted for 80% of fake news source exposures, and 0.1% accounted for nearly 80% of fake news sources shared. Individuals most likely to engage with fake news sources were conservative leaning, older, and highly engaged with political news. A cluster of fake news sources shared overlapping audiences on the extreme right, but for people across the political spectrum, most political news exposure still came from mainstream media outlets.

Fake news on Twitter during the 2016 U.S. presidential election.

Twitter has become a pervasive tool in election campaigns. Candidates, parties, journalists, and a steadily increasing share of the public are using Twitter to comment on, interact around, and research public reactions to politics. These uses have met with growing scholarly attention. As of now, this research is fragmented, lacks a common body of evidence, and shared approaches to data collection and selection. This article presents the results of a systematic literature review of 127 studies addressing the use of Twitter in election campaigns. In this systematic review, I will discuss the available research with regard to findings on the use of Twitter by parties, candidates, and publics during election campaigns and during mediated campaign events. Also, I will address prominent research designs and approaches to data collection and selection.

Twitter use in election campaigns: A systematic literature review

We demonstrate that exposure to the news media causes Americans to take public stands on specific issues, join national policy conversations, and express themselves publicly—all key components of democratic politics—more often than they would otherwise. After recruiting 48 mostly small media outlets, we chose groups of these outlets to write and publish articles on subjects we approved, on dates we randomly assigned. We estimated the causal effect on proximal measures, such as website pageviews and Twitter discussion of the articles’ specific subjects, and distal ones, such as national Twitter conversation in broad policy areas. Our intervention increased discussion in each broad policy area by ~62.7% (relative to a day’s volume), accounting for 13,166 additional posts over the treatment week, with similar effects across population subgroups.

https://science.sciencemag.org/content/sci/358/6364/776.full.pdf

How the news media activate public expression and influence national agendas.

Spatially or temporally dense polling remains both difficult and expensive using existing survey methods. In response, there have been increasing efforts to approximate various survey measures using social media, but most of these approaches remain methodologically flawed. To remedy these flaws, this article combines 1,200 state-level polls during the 2012 presidential campaign with over 100 million state-located political tweets; models the polls as a function of the Twitter text using a new linear regularization feature-selection method; and shows via out-of-sample testing that when properly modeled, the Twitter-based measures track and to some degree predict opinion polls, and can be extended to unpolled states and potentially substate regions and subday timescales. An examination of the most predictive textual features reveals the topics and events associated with opinion shifts, sheds light on more general theories of partisan difference in attention and information processing, and may be of use for real-time campaign strategy.

http://www.kenbenoit.net/pdfs/NDATAD2013/Beauchamp_twitterpolls_2.pdf

Predicting and Interpolating State-Level Polls Using Twitter Textual Data

Debate and deliberation play essential roles in politics and government, but most models presume that debates are won mainly via superior style or agenda control Ideally, however, debates would be won on the merits, as a function of which side has the stronger arguments We propose a predictive model of debate that estimates the effects of linguistic features and the latent persuasive strengths of different topics, as well as the interactions between the two Using a dataset of 118 Oxford-style debates, our model's combination of content (as latent topics) and style (as linguistic features) allows us to predict audience-adjudicated winners with 74% accuracy, significantly outperforming linguistic features alone (66%) Our model finds that winning sides employ stronger arguments, and allows us to identify the linguistic features associated with strong or weak arguments

/pdf/winning-on-the-merits-the-joint-effects-of-content-and-style-3l6yesui1g.pdf

Winning on the Merits: The Joint Effects of Content and Style on Debate Outcomes

Measuring public opinion with social media data

Millions of conversations are generated every day on social media platforms. With limited attention, it is challenging for users to select which discussions they would like to participate in. Here we propose a new method for microblog conversation recommendation. While much prior work has focused on post-level recommendation, we exploit both the conversational context, and user content and behavior preferences. We propose a statistical model that jointly captures: (1) topics for representing user interests and conversation content, and (2) discourse modes for describing user replying behavior and conversation dynamics. Experimental results on two Twitter datasets demonstrate that our system outperforms methods that only model content without considering discourse.

/pdf/microblog-conversation-recommendation-via-joint-modeling-of-ncbxtkybek.pdf

Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse

In recent years, a growing body of empirical research suggests that indiscriminate violence against civilian targets tends to carry substantial political risks compared to more selective violence against military targets. To better understand why terrorist groups sometimes attack politically suboptimal targets, scholars are increasingly adopting a principal-agent framework where the leaders of terrorist groups are understood as principals and lower level members as agents. According to this framework, terrorist leaders are thought to behave as essentially rational political actors, whereas lower level members are believed to harbor stronger non-political incentives for harming civilians, often in defiance of leadership preferences. We test this proposition with an original content analysis of terrorist propaganda videos. Consistent with the principal–agent framework, our analysis demonstrates statistically that terrorist leaders tend to favor significantly less indiscriminate violence than their o...

Nicholas Beauchamp

Papers

Predicting and Interpolating State-Level Polls Using Twitter Textual Data

Winning on the Merits: The Joint Effects of Content and Style on Debate Outcomes

Measuring public opinion with social media data

Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse

What Terrorist Leaders Want: A Content Analysis of Terrorist Propaganda Videos