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A review of social media-based public opinion analyses: Challenges and recommendations

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  • Dong, Xuefan
  • Lian, Ying

Abstract

Compared with survey polls, social media can yield a better and more comprehensive understanding of public perceptions of special topics in a more scientific manner. However, despite this advantage, there seem to be limited investigations into the challenges in social media-based public opinion analysis. This study offers an understanding of the challenges in this field and some corresponding recommendations. Through a systematic literature review, we identify 54 papers to analyze and discuss issues related to data collection, data quality, and data mining. This paper summarizes a framework for social media-based public opinion analysis as well as the commonly employed data mining methodologies. We found that collecting public opinion data from Facebook and Weibo is difficult because of their restricted application programming interface and measures against Web Crawler. How to effectively and conveniently delete invalid data and how to design data mining methods for social media data, especially for those in Chinese, are still two main challenges in social media-based public opinion analysis. We claim that using multiple data sources, optimizing keyword settings, enhancing interdisciplinary cooperation, and paying more attention to the functional role of social media can benefit the development of social media-based public opinion analysis. This study also highlights the potential risks of releasing the personal information of the public in the use of social media data in research.

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  • Dong, Xuefan & Lian, Ying, 2021. "A review of social media-based public opinion analyses: Challenges and recommendations," Technology in Society, Elsevier, vol. 67(C).
    • Handle: RePEc:eee:teinso:v:67:y:2021:i:c:s0160791x21001998
    DOI: 10.1016/j.techsoc.2021.101724
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