人气
社会化媒体
悲伤
领域(数学)
数据科学
系统回顾
斯科普斯
差异(会计)
心理学
负偏倚
计算机科学
情绪分析
经验抽样法
光学(聚焦)
认知心理学
自动汇总
计算社会学
媒体偏见
政治
叙述的
互联网
叙述性评论
众包
荟萃分析
确认偏差
科学网
出版偏见
同行评审
情报检索
社会学
万维网
负效应
标识
DOI:10.1108/oir-06-2025-0472
摘要
Purpose Since the widespread uptake of social media in the 2010s, research on emotion and sentiment in online sharing has grown rapidly across various fields. Yet, this expansion lacks standardized methods and shared concepts, resulting in fragmented evidence and even contradictory findings on the direction of these effects. No systematic synthesis across fields currently exists. This review fills that gap by offering the first comprehensive overview of findings, scope, and methodologies at a pivotal moment before large language models reshape this research landscape. Design/methodology/approach The systematic review identified 136 studies from 1,181 search results retrieved via Web of Science and Scopus in accordance with the PRISMA guidelines. Given the heterogeneity of the studies, the synthesis is presented in narrative form. Findings Emotions and sentiment, in general, increase the popularity of information across platforms. The effect is found most consistently for negative discrete emotions, such as anger, anxiety, sadness and fear. Positivity bias is often identified in health communication, while negativity bias is prevalent in political communication. Going forward, the field should focus on sampling across platforms, greater linguistic diversity, and analysis of visual material. Originality/value The systematic review provides the first synthesis of a rapidly expanding field relevant to scholars across disciplines. It identifies potential sources of variance in the results and highlights methodological heterogeneity and identifies paths for future research.
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