13,205 research outputs found
Determining the Function of Political Tweets
We study the discursive practices of politicians and journalists on social media. For this we need more annotated data than we currently have but the annotation process is time-consuming and costly. In this paper we examine machine learning methods for automatically annotating unseen tweetsbased on a small set of manually annotated tweets. Forimproving the performance of the learner, we focus onmethods related to training data expansion, like artificialtraining data, active learning and incorporating languagemodels developed from unannotated text
Environmental Organizationsâ Litigation Communication in the Polarized U.S. Political Landscape
This study analyzes environmental litigation communication in an increasingly polarized political context. Specifically, this project analyzes environmental organizationsâ communication strategies and messages related to their litigation efforts in order to better understand how environmental nonprofits frame environmental litigation within the current U. S. political landscape. Multiple data sources (e.g., website content, tweets, and interviews) triangulate the study by providing varying strategic perspectives on organizationsâ environmental litigation communication efforts. Results show that nonprofit organizations like the National Resources Defense Council and Sierra Club use a variety of frames that portray litigation as a righteous action used to hold those in power to account, targeting not only large, polluting corporations but also the U.S. Environmental Protection Agency currently run by the Trump Administration
Evolution of Online User Behavior During a Social Upheaval
Social media represent powerful tools of mass communication and information
diffusion. They played a pivotal role during recent social uprisings and
political mobilizations across the world. Here we present a study of the Gezi
Park movement in Turkey through the lens of Twitter. We analyze over 2.3
million tweets produced during the 25 days of protest occurred between May and
June 2013. We first characterize the spatio-temporal nature of the conversation
about the Gezi Park demonstrations, showing that similarity in trends of
discussion mirrors geographic cues. We then describe the characteristics of the
users involved in this conversation and what roles they played. We study how
roles and individual influence evolved during the period of the upheaval. This
analysis reveals that the conversation becomes more democratic as events
unfold, with a redistribution of influence over time in the user population. We
conclude by observing how the online and offline worlds are tightly
intertwined, showing that exogenous events, such as political speeches or
police actions, affect social media conversations and trigger changes in
individual behavior.Comment: Best Paper Award at ACM Web Science 201
Identifying Users with Opposing Opinions in Twitter Debates
In recent times, social media sites such as Twitter have been extensively
used for debating politics and public policies. These debates span millions of
tweets and numerous topics of public importance. Thus, it is imperative that
this vast trove of data is tapped in order to gain insights into public opinion
especially on hotly contested issues such as abortion, gun reforms etc. Thus,
in our work, we aim to gauge users' stance on such topics in Twitter. We
propose ReLP, a semi-supervised framework using a retweet-based label
propagation algorithm coupled with a supervised classifier to identify users
with differing opinions. In particular, our framework is designed such that it
can be easily adopted to different domains with little human supervision while
still producing excellent accuracyComment: Corrected typos in Section 4, under "Visibly Opinionated Users". The
numbers did not add up. Results remain unchange
Identifying communicator roles in Twitter
Twitter has redefined the way social activities can be coordinated; used for mobilizing people during natural disasters, studying health epidemics, and recently, as a communication platform during social and political change. As a large scale system, the volume of data transmitted per day presents Twitter users with a problem: how can valuable content be distilled from the back chatter, how can the providers of valuable information be promoted, and ultimately how can influential individuals be identified?To tackle this, we have developed a model based upon the Twitter message exchange which enables us to analyze conversations around specific topics and identify key players in a conversation. A working implementation of the model helps categorize Twitter users by specific roles based on their dynamic communication behavior rather than an analysis of their static friendship network. This provides a method of identifying users who are potentially producers or distributers of valuable knowledge
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