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Shifting to negativity is more and more prevalent in online communities and may play a key role in group polarization. While current research indicates a close relationship between group polarization and negative sentiment, they often link negative sentiment shifts with echo chambers and misinformation within echo chambers. In this work, we explore the sentiment drift using over 4 million comments from a Chinese online movie-rating community that is less affected by misinformation than other mainstream online communities and has no echo chamber structures. We measure the sentiment shift of the community and users of different engagement levels. Our analysis reveals that while the community does not show a tendency toward negativity, users of higher engagement levels are generally more negative, considering factors like the different movies they consume. The results indicate a fitting-in process, suggesting the possible mechanism of group identity on sentiment shift on social media platforms. These findings also provide guidance on web design to tackle the negativity issue and expand sentiment shift analysis to non-English contexts.
Shifting to negativity is more and more prevalent in online communities and may play a key role in group polarization. While current research indicates a close relationship between group polarization and negative sentiment, they often link negative sentiment shifts with echo chambers and misinformation within echo chambers. In this work, we explore the sentiment drift using over 4 million comments from a Chinese online movie-rating community that is less affected by misinformation than other mainstream online communities and has no echo chamber structures. We measure the sentiment shift of the community and users of different engagement levels. Our analysis reveals that while the community does not show a tendency toward negativity, users of higher engagement levels are generally more negative, considering factors like the different movies they consume. The results indicate a fitting-in process, suggesting the possible mechanism of group identity on sentiment shift on social media platforms. These findings also provide guidance on web design to tackle the negativity issue and expand sentiment shift analysis to non-English contexts.
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The author would like to sincerely thank Professor Sanja Miklin for providing many constructive comments on an earlier draft, Professor Zhao Wang and James Evans for their valuable suggestions on methodological improvements, Stephen Parkin for refining the language, and anonymous reviewers for their constructive feedback. The author would also like to thank Hongding Zhu, Henry Lin, Rui Pan, and Peihan Gao for their passionate discussions and encouragement and the developers who generously shared the dataset.
The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).