{"title":"政治内容参与模型:大规模分析TikTok政治视频内容特征和受众参与","authors":"Zicheng Cheng , Yanlin Li","doi":"10.1016/j.chb.2025.108808","DOIUrl":null,"url":null,"abstract":"<div><div>TikTok has emerged as a prominent platform for political information dissemination, where traditional news organizations, political figures, grassroots organizations, and influencers engage audiences on political and civic issues. However, limited research has systematically examined why politically oriented TikTok videos attract engagement. This study introduces the Political Content Engagement Model (PCEM), which explains how political identity, content features, content sources, and topic issues influence engagement. Using a dataset of 578,420 TikTok videos posted by 9722 elite accounts, we use machine learning and topic modeling to analyze how features such as political party references, issue framing, justification, sentiment, civility, and mobilization appeals affect video engagement. Besides, we investigate differences in engagement patterns between liberal- and conservative-leaning TikTok accounts and differentiate between internal and external engagement behaviors. Across both liberal and conservative accounts, civility level and out-party critique consistently emerge as the most powerful predictors of political TikTok video engagement. Our findings contribute to the field of digital political communication by offering insights into TikTok users’ political engagement behavior on TikTok and how different content strategies drive audience interactions.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"174 ","pages":"Article 108808"},"PeriodicalIF":8.9000,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Political Content Engagement Model: A large-scale analysis of TikTok political video content features and audience engagement\",\"authors\":\"Zicheng Cheng , Yanlin Li\",\"doi\":\"10.1016/j.chb.2025.108808\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>TikTok has emerged as a prominent platform for political information dissemination, where traditional news organizations, political figures, grassroots organizations, and influencers engage audiences on political and civic issues. However, limited research has systematically examined why politically oriented TikTok videos attract engagement. This study introduces the Political Content Engagement Model (PCEM), which explains how political identity, content features, content sources, and topic issues influence engagement. Using a dataset of 578,420 TikTok videos posted by 9722 elite accounts, we use machine learning and topic modeling to analyze how features such as political party references, issue framing, justification, sentiment, civility, and mobilization appeals affect video engagement. Besides, we investigate differences in engagement patterns between liberal- and conservative-leaning TikTok accounts and differentiate between internal and external engagement behaviors. Across both liberal and conservative accounts, civility level and out-party critique consistently emerge as the most powerful predictors of political TikTok video engagement. Our findings contribute to the field of digital political communication by offering insights into TikTok users’ political engagement behavior on TikTok and how different content strategies drive audience interactions.</div></div>\",\"PeriodicalId\":48471,\"journal\":{\"name\":\"Computers in Human Behavior\",\"volume\":\"174 \",\"pages\":\"Article 108808\"},\"PeriodicalIF\":8.9000,\"publicationDate\":\"2025-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers in Human Behavior\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0747563225002559\",\"RegionNum\":1,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers in Human Behavior","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0747563225002559","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, EXPERIMENTAL","Score":null,"Total":0}
Political Content Engagement Model: A large-scale analysis of TikTok political video content features and audience engagement
TikTok has emerged as a prominent platform for political information dissemination, where traditional news organizations, political figures, grassroots organizations, and influencers engage audiences on political and civic issues. However, limited research has systematically examined why politically oriented TikTok videos attract engagement. This study introduces the Political Content Engagement Model (PCEM), which explains how political identity, content features, content sources, and topic issues influence engagement. Using a dataset of 578,420 TikTok videos posted by 9722 elite accounts, we use machine learning and topic modeling to analyze how features such as political party references, issue framing, justification, sentiment, civility, and mobilization appeals affect video engagement. Besides, we investigate differences in engagement patterns between liberal- and conservative-leaning TikTok accounts and differentiate between internal and external engagement behaviors. Across both liberal and conservative accounts, civility level and out-party critique consistently emerge as the most powerful predictors of political TikTok video engagement. Our findings contribute to the field of digital political communication by offering insights into TikTok users’ political engagement behavior on TikTok and how different content strategies drive audience interactions.
期刊介绍:
Computers in Human Behavior is a scholarly journal that explores the psychological aspects of computer use. It covers original theoretical works, research reports, literature reviews, and software and book reviews. The journal examines both the use of computers in psychology, psychiatry, and related fields, and the psychological impact of computer use on individuals, groups, and society. Articles discuss topics such as professional practice, training, research, human development, learning, cognition, personality, and social interactions. It focuses on human interactions with computers, considering the computer as a medium through which human behaviors are shaped and expressed. Professionals interested in the psychological aspects of computer use will find this journal valuable, even with limited knowledge of computers.