Interdisciplinary Laboratory of Computational Social Science
Social media has become a prominent device to organize social protests, deliver information to citizens, and develop collective narratives. However, not all messages spread equally across social media nor do they gain the same level of acceptance. Analyzing in detail social media data, our research explores a broad range of relevant social topics using cutting-edge computational methods. In a set of distinct projects, we discuss the relationship between the propagation of political messages, political polarization, and the integration of new media markets connecting social media users and traditional news organizations; explore the behavior of outlets' editors when responding to users preferences in a social media environment, and propose new methods to take in consideration spatial contagious, heterogeneous effects, and autocorrelation when dealing with big network data. Below, we present some of the publications related to this project.
In an environment where readers react on real time to publications, how should editors decide the editorial line of their news organizations? In this article, we consider a model where readers ``vote'' on social media by embedding links to traditional media and where journalists modify their news content to maximize readership. Following an extensive literature on spatial models of voting, we show that quality outlets should concentrate on salient issues and editorialize their news to approximate the ideological preferences of users who assign the highest reputation scores to its products.
Low quality outlets, we show, are crowded out to more marginal ideological locations and to more marginal issues. Our results provide a general theory to describe how new technologies shape the editorial decisions of news organizations.
In social media, sharing posts exposes a larger number of users to the preferred content
of their peers. As users select or discard content, they collectively highlight facets of
events or issues as to promote a particular interpretation. This article describes how
social media users frame political events by selectively sharing content that is cognitively
congruent with their beliefs. We model cognitive dissonance modeling time-to-retweet
and exemplify the proposed theory with a study of recent protest events in Argentina.
Twitter data are becoming an important part of modern political science research, but key
aspects of the inner workings of Twitter streams as well as self-censorship on the platform
require further research. A particularly important research agenda is to understand removal
rates of politically charged tweets. In this article, I provide a strategy to understand removal
rates on Twitter, particularly on politically charged topics. First, the technical properties of
Twitter's API that may distort the analyses of removal rates are tested. Results show that
the forward stream does not capture every possible tweet ±between 2 and 5 percent of
tweets are lost on average, even when the volume of tweets is low and the firehose not
needed. Second, data from Twitter's streams are collected on contentious topics such as
terrorism or political leaders and non-contentious topics such as types of food. The statistical
technique used to detect uncommon removal rate patterns is multilevel analysis. Results
show significant differences in the removal of tweets between different topic groups. This
article provides the first systematic comparison of information loss and removal on Twitter
as well as a strategy to collect valid removal samples of tweets.