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| Topic Detection and Evolution in Social Media Platforms Based on a Temporal Co-word Network |
| Yang Xinyi1, Wang Wei1, Zhu Hengmin2 |
1.School of Information Management, Nanjing University, Nanjing 210023
2.School of Management, Nanjing University of Posts and Telecommunications, Nanjing 210003 |
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Abstract Social media platforms are essential for netizens to express opinions and sentiments. Analyzing topic distribution and their evolution on social media platforms can reveal hot topics and their changes to provide important references for influencing public opinion. This study employs network community evolution discovery to detect topics and analyze their evolution on social media platforms. First, user-generated textual contents are divided into several slices to construct a temporal co-word network, and the backbones of co-word network in each time slice are extracted. Then, network communities are discovered through the Leiden algorithm to represent topics. To detect topic evolution, a method of detecting topic evolution events is proposed on the basis of the forward and backward transfer probabilities and community size. Therefore, events, such as continuing, growing, shrinking, merging, splitting, forming, and dissolving are identified. Considering the microblogs about COVID-19 in Sina Weibo as an example, a larger number of topics, with finer granularity are uncovered in backbones than in the original co-word networks. Topic evolution paths are also found, such as changes in users’ sentiment from negative to positive, professionalization of pandemic prevention and medical work, the global spread of the pandemic, and the growing economic impact of the pandemic.
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Received: 05 May 2022
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