Characterizing Partisan Political Narrative Frameworks about COVID-19 on Twitter
Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington（印第安纳大学伯明顿分校卢迪信息学、计算与工程学院复杂网络与系统研究中心）；Network Science Institute, Indiana University, Bloomington (IUNI)（印第安纳大学布卢明顿分校网络科学研究所）；Connection Science, Massachusetts Institute of Technology（麻省理工学院连接科学中心）；Sirius XM（天狼星XM公司）
Elise Jing, Yong-Yeol Ahn
备注：
16 pages, 5 figures. To be published in EPJ Data Science
The COVID-19 pandemic is a global crisis that has been testing every society and exposing the critical role of local politics in crisis response. In the United States, there has been a strong partisan divide between the Democratic and Republican party's narratives about the pandemic which resulted in polarization of individual behaviors and divergent policy adoption across regions. As shown in this case, as well as in most major social issues, strongly polarized narrative frameworks facilitate such narratives. To understand polarization and other social chasms, it is critical to dissect these diverging narratives. Here, taking the Democratic and Republican political social media posts about the pandemic as a case study, we demonstrate that a combination of computational methods can provide useful insights into the different contexts, framing, and characters and relationships that construct their narrative frameworks which individual posts source from. Leveraging a dataset of tweets from elite politicians in the U.S., we found that the Democrats' narrative tends to be more concerned with the pandemic as well as financial and social support, while the Republicans discuss more about other political entities such as China. We then perform an automatic framing analysis to characterize the ways in which they frame their narratives, where we found that the Democrats emphasize the government's role in responding to the pandemic, and the Republicans emphasize the roles of individuals and support for small businesses. Finally, we present a semantic role analysis that uncovers the important characters and relationships in their narratives as well as how they facilitate a membership categorization process. Our findings concretely expose the gaps in the "elusive consensus" between the two parties. Our methodologies may be applied to computationally study narratives in various domains.
With the significant increase in users on social media platforms, a new means of political campaigning has appeared. Twitter and Facebook are now notable campaigning tools during elections. Indeed, the candidates and their parties now take to the internet to interact and spread their ideas. In this paper, we aim to identify political communities formed on Twitter during the 2022 French presidential election and analyze each respective community. We create a large-scale Twitter dataset containing 1.2 million users and 62.6 million tweets that mention keywords relevant to the election. We perfor…
MIT Center for Constructive Communication（麻省理工学院建设性沟通中心）；MIT Media Lab（麻省理工学院媒体实验室）
As political attitudes have diverged ideologically in the United States, political speech has diverged lingusitically. The ever-widening polarization between the US political parties is accelerated by an erosion of mutual understanding between them. We aim to make these communities more comprehensible to each other with a framework that probes community-specific responses to the same survey questions using community language models CommunityLM. In our framework we identify committed partisan members for each community on Twitter and fine-tune LMs on the tweets authored by them. We then assess …
Putnam Science Academy（普特南科学院）；Princeton High School（普林斯顿高中）；Zhengzhou University（郑州大学）；Huazhong University of Science and Technology（华中科技大学）
This study verified the effectiveness of Donald Trump's Twitter campaign in guiding agen-da-setting and deflecting political risk and examined Trump's Twitter communication strategy and explores the communication effects of his tweet content during Covid-19 pandemic. We collected all tweets posted by Trump on the Twitter platform from January 1, 2020 to December 31, 2020.We used Ordinary Least Squares (OLS) regression analysis with a fixed effects model to analyze the existence of the Twitter strategy. The correlation between the number of con-firmed daily Covid-19 diagnoses and the number of …
Dedan Kimathi University of Technology（德丹·基马蒂科技大学）
In this new era of social media, social networks are becoming increasingly important sources of user-generated content on the internet. These kinds of information resources, which include a lot of people's feelings, opinions, feedback, and reviews, are very useful for big businesses, markets, politics, journalism, and many other fields. Politics is one of the most talked-about and popular topics on social media networks right now. Many politicians use micro-blogging services like Twitter because they have a large number of followers and supporters on those networks. Politicians, political part…
Influencers are key to the nature and networks of information propagation on social media. Influencers are particularly important in political discourse through their engagement with issues, and may derive their legitimacy either solely or in large part through online operation, or have an offline sphere of expertise such as entertainers, journalists etc. To quantify influencers' political engagement and polarity, we use Google's Universal Sentence Encoder (USE) to encode the tweets of 6k influencers and 26k Indian politicians during political crises in India. We then obtain aggregate vector r…
Underscore（Underscore公司）；KAIST（韩国科学技术院）；Seoul National University（首尔国立大学）
Vaccine adverse events have been presumed to be a relatively objective measure that is immune to political polarization. The real-world data, however, shows the correlation between presidential disapproval ratings and the subjective severity of adverse events. This paper investigates the partisan bias in COVID vaccine adverse events coverage with language models that can classify the topic of vaccine-related articles and the political disposition of news comments. Based on 90K news articles from 52 major newspaper companies, we found that conservative media are inclined to report adverse event…
Wroclaw University of Science and Technology（弗罗茨瓦夫理工大学）
Every day, the world is flooded by millions of messages and statements posted on Twitter or Facebook. Social media platforms try to protect users' personal data, but there still is a real risk of misuse, including elections manipulation. Did you know, that only 13 posts addressing important or controversial topics for society are enough to predict one's political affiliation with a 0.85 F1-score? To examine this phenomenon, we created a novel universal method of semi-automated political leaning discovery. It relies on a heuristical data annotation procedure, which was evaluated to achieve 0.95…
HBKU（哈马德·本·哈利法大学）；Qatar Computing Research Institute（卡塔尔计算研究所）
Through the use of Twitter, framing has become a prominent presidential campaign tool for politically active users. Framing is used to influence thoughts by evoking a particular perspective on an event. In this paper, we show that the COVID19 pandemic rather than being viewed as a public health issue, political rhetoric surrounding it is mostly shaped through a blame frame (blame Trump, China, or conspiracies) and a support frame (support candidates) backing the agenda of Republican and Democratic users in the lead up to the 2020 presidential campaign. We elucidate the divergences between supp…
PartisanLens: A Multilingual Dataset of Hyperpartisan and Conspiratorial Immigration Narratives in European Media
Centro Singular de Investigación en Tecnoloxías Intelixentes da USC（乌拉特大学智能技术研究中心）；IRLab, CITIC Research Centre, Universidade da Coruña（IR实验室，CITIC研究中心，科鲁纳大学）；Universidade de Évora（埃夫拉大学）；Universidad de La Rioja（里瓦达维亚大学）；GESIS Leibniz Institute for the Social Sciences（莱比锡社会科学院）
This article presents a preliminary approach towards characterizing political fake news on Twitter through the analysis of their meta-data. In particular, we focus on more than 1.5M tweets collected on the day of the election of Donald Trump as 45th president of the United States of America. We use the meta-data embedded within those tweets in order to look for differences between tweets containing fake news and tweets not containing them. Specifically, we perform our analysis only on tweets that went viral, by studying proxies for users' exposure to the tweets, by characterizing accounts spre…
This work presents a supervised method for generating a classifier model of the stances held by Chinese-speaking politicians and other Twitter users. Many previous works of political tweets prediction exist on English tweets, but to the best of our knowledge, this is the first work that builds prediction model on Chinese political tweets. It firstly collects data by scraping tweets of famous political figure and their related users. It secondly defines the political spectrum in two groups: the group that shows approvals to the Chinese Communist Party and the group that does not. Since there ar…
Measuring Dimensions of Self-Presentation in Twitter Bios and their Links to Misinformation Sharing
University at Buffalo（布法罗大学）；GESIS Leibniz Institute for the Social Sciences（GESIS莱布尼茨社会科学研究所）；University of Pittsburgh（匹兹堡大学）；George Washington University（乔治华盛顿大学）；Northeastern University（东北大学）
Popularity is a critical success factor for a politician and her/his party to win in elections and implement their plans. Finding the reasons behind the popularity can provide a stable political movement. This research attempts to measure popularity in Twitter using a mixed method. In recent years, Twitter data has provided an excellent opportunity for exploring public opinions by analyzing a large number of tweets. This study has collected and examined 4.5 million tweets related to a US politician, Senator Bernie Sanders. This study investigated eight economic reasons behind the senator's pop…
This study aims to understand the South African political context by analysing the sentiments shared on Twitter during the local government elections. An emphasis on the analysis was placed on understanding the discussions led around four predominant political parties ANC, DA, EFF and ActionSA. A semi-supervised approach by means of a graph-based technique to label the vast accessible Twitter data for the classification of tweets into negative and positive sentiment was used. The tweets expressing negative sentiment were further analysed through latent topic extraction to uncover hidden topics…
