Two Computational Models for Analyzing Political Attention in Social Media

Understanding how political attention is divided and over what subjects is crucial for research on areas such as agenda setting, framing, and political rhetoric. However, existing methods for measuring attention, such as manual labeling ac- cording to established codebooks, are expensive and restric- tive. We describe two computational models that automati- cally distinguish topics in politicians’ social media content. Our models - one supervised classifier and one unsupervised topic model - provide different benefits. The supervised clas- sifier reduces the labor required to classify content accord- ing to pre-determined topic lists. However, tweets do more than communicate policy positions. Our unsupervised model uncovers both political topics and other Twitter uses (e.g., constituent service). Together, these models are effective, in- expensive computational tools for political communication and social media research. We demonstrate their utility and discuss the different analyses they afford by applying both models to the tweets posted by members of the 115th U.S. Congress.This material is based upon work supported by the National Science Foundation under Grant No. 1822228.https://deepblue.lib.umich.edu/bitstream/2027.42/147460/6/Hemphill and Schopke - Two Compuational Models.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147460/1/Hemphill and Schopke - Two Computational Models.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147460/8/ICWSM 2020 Two Computational Models.pptx5056Description of Hemphill and Schopke - Two Compuational Models.pdf : Revised articleDescription of Hemphill and Schopke - Two Computational Models.pdf : Main articleDescription of ICWSM 2020 Two Computational Models.pptx : Presentation with scrip

Polarization and acculturation in US Election 2016 outcomes – Can twitter analytics predict changes in voting preferences

Elections are among the most critical events in a national calendar. During elections, candidates increasingly use social media platforms to engage voters. Using the 2016 US presidential election as a case study, we looked at the use of Twitter by political campaigns and examined how the drivers of voter behaviour were reflected in Twitter. Social media analytics have been used to derive insights related to theoretical frameworks within political science. Using social media analytics, we investigated whether the nature of social media discussions have an impact on voting behaviour during an election, through acculturation of ideologies and polarization of voter preferences. Our findings indicate that discussions on Twitter could have polarized users significantly. Reasons behind such polarization were explored using Newman and Sheth's model of voter's choice behaviour. Geographical analysis of tweets, users, and campaigns suggests acculturation of ideologies among voting groups. Finally, network analysis among voters indicates that polarization may have occurred due to differences between the respective online campaigns. This study thus provides important and highly relevant insights into voter behaviour for the future management and governance of successful political campaigns.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Information and Communication Technolog

Detecting Political Framing Shifts and the Adversarial Phrases within\\ Rival Factions and Ranking Temporal Snapshot Contents in Social Media

abstract: Social Computing is an area of computer science concerned with dynamics of communities and cultures, created through computer-mediated social interaction. Various social media platforms, such as social network services and microblogging, enable users to come together and create social movements expressing their opinions on diverse sets of issues, events, complaints, grievances, and goals. Methods for monitoring and summarizing these types of sociopolitical trends, its leaders and followers, messages, and dynamics are needed. In this dissertation, a framework comprising of community and content-based computational methods is presented to provide insights for multilingual and noisy political social media content. First, a model is developed to predict the emergence of viral hashtag breakouts, using network features. Next, another model is developed to detect and compare individual and organizational accounts, by using a set of domain and language-independent features. The third model exposes contentious issues, driving reactionary dynamics between opposing camps. The fourth model develops community detection and visualization methods to reveal underlying dynamics and key messages that drive dynamics. The final model presents a use case methodology for detecting and monitoring foreign influence, wherein a state actor and news media under its control attempt to shift public opinion by framing information to support multiple adversarial narratives that facilitate their goals. In each case, a discussion of novel aspects and contributions of the models is presented, as well as quantitative and qualitative evaluations. An analysis of multiple conflict situations will be conducted, covering areas in the UK, Bangladesh, Libya and the Ukraine where adversarial framing lead to polarization, declines in social cohesion, social unrest, and even civil wars (e.g., Libya and the Ukraine).Dissertation/ThesisDoctoral Dissertation Computer Science 201