{"title":"Can Social Media Engagement Predict Election Results? Bandwagon Effects of Tweets About US Senate Candidates","authors":"Jinping Wang, S. Shyam Sundar, Nilàm Ram","doi":"10.1177/20563051241298449","DOIUrl":null,"url":null,"abstract":"The social media platform X (formerly Twitter) has grown to become an important venue for political discourse, with candidates using it integrally in their election campaigns. However, it is not clear if activity on Twitter can be used to forecast elections, given conflicting findings in the literature. By analyzing 830,796 tweets mentioning key hashtags related to nine US senate races in 2014, 2016, and 2018, we demonstrate that cascades in volume and sentiment of tweets between September 1 and Election Day can predict election outcomes. We developed a non-linear growth modeling tool to identify the point in time at which bandwagon support for competing candidates begins to diverge. We also discovered that bot-driven tweets play a negligible role. We discuss theoretical and practical implications for both computational research and media effects, showing the value of combining big-data analysis and longitudinal non-linear dynamics to study the relationship between social media activity and real-world outcomes.","PeriodicalId":47920,"journal":{"name":"Social Media + Society","volume":"253 1","pages":""},"PeriodicalIF":5.5000,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Social Media + Society","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.1177/20563051241298449","RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMMUNICATION","Score":null,"Total":0}
引用次数: 0
Abstract
The social media platform X (formerly Twitter) has grown to become an important venue for political discourse, with candidates using it integrally in their election campaigns. However, it is not clear if activity on Twitter can be used to forecast elections, given conflicting findings in the literature. By analyzing 830,796 tweets mentioning key hashtags related to nine US senate races in 2014, 2016, and 2018, we demonstrate that cascades in volume and sentiment of tweets between September 1 and Election Day can predict election outcomes. We developed a non-linear growth modeling tool to identify the point in time at which bandwagon support for competing candidates begins to diverge. We also discovered that bot-driven tweets play a negligible role. We discuss theoretical and practical implications for both computational research and media effects, showing the value of combining big-data analysis and longitudinal non-linear dynamics to study the relationship between social media activity and real-world outcomes.
期刊介绍：
Social Media + Society is an open access, peer-reviewed scholarly journal that focuses on the socio-cultural, political, psychological, historical, economic, legal and policy dimensions of social media in societies past, contemporary and future. We publish interdisciplinary work that draws from the social sciences, humanities and computational social sciences, reaches out to the arts and natural sciences, and we endorse mixed methods and methodologies. The journal is open to a diversity of theoretic paradigms and methodologies. The editorial vision of Social Media + Society draws inspiration from research on social media to outline a field of study poised to reflexively grow as social technologies evolve. We foster the open access of sharing of research on the social properties of media, as they manifest themselves through the uses people make of networked platforms past and present, digital and non. The journal presents a collaborative, open, and shared space, dedicated exclusively to the study of social media and their implications for societies. It facilitates state-of-the-art research on cutting-edge trends and allows scholars to focus and track trends specific to this field of study.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
