Mechanism of corrections to false information in online social network

doi:10.3785/j.issn.1008-973X.2021.04.003

Journal of ZheJiang University (Engineering Science)

2021, Vol. 55

Issue (4): 615-625 DOI: 10.3785/j.issn.1008-973X.2021.04.003

Mechanism of corrections to false information in online social network

Yu-qi ZHANG(

),Bin GUO*(

),Ya-san DING,Si-cong LIU,Zhi-wen YU

School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China

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Abstract

The mechanism of corrections on false information in real social network environments was analyzed. The effect of corrections was evaluated and its influencing factors were explored. Eight factors that affect the effectiveness of correction were summarized based on existing research and our hypotheses, such as the proportion of the original false information, whether it contains text warnings of false information, whether to explain the explanation, user influence, etc.. The effectiveness of correction posts was evaluated by sentiment analysis and the social context of themselves. Statistical methods were used to test the relationship between the pre-determined influencing factors and the effectiveness of correction. The experiment was conducted based on the false information data about COVID-19 epidemic collected from Sina Weibo. Results show that a higher proportion of false information in a correction reduces the effectiveness, and explaining the reason improves the effectiveness. Six conclusions that improve the effectiveness of corrections on social networks were proposed such as mentioning original misinformation less, explaining why original misinformation is wrong. Guidance was provided for related media to correct false information on social network.

Key words： false information mechanism of correction sentiment analysis social network Weibo

Received: 27 January 2021 Published: 07 May 2021

CLC:

TP 399

Fund: 国家重点研发计划资助项目（2019QY0600）；国家自然科学基金资助项目（61772428，61725205）

Corresponding Authors: Bin GUO E-mail: 1347088657@qq.com;guobin.keio@gmail.com

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Cite this article:

Yu-qi ZHANG,Bin GUO,Ya-san DING,Si-cong LIU,Zhi-wen YU. Mechanism of corrections to false information in online social network. Journal of ZheJiang University (Engineering Science), 2021, 55(4): 615-625.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2021.04.003 OR http://www.zjujournals.com/eng/Y2021/V55/I4/615

社交网络假消息辟谣作用机理

研究真实社交网络环境下假消息辟谣作用机理. 提出评估辟谣效果的方法及探究影响辟谣效果的因素. 基于已有研究成果与假设，总结出8个影响辟谣效果的因素，如原假消息内容占比、是否包含谣言文字警示、是否解释原因、用户影响力等. 使用情感分析和微博社交上下文，评估辟谣微博的辟谣效果. 利用统计学方法，检验预设影响因素与辟谣效果间的关系. 基于新冠疫情相关的辟谣微博数据开展实验，实验分析表明，辟谣信息中原假消息内容占比和辟谣效果呈负相关，解释原因与辟谣效果呈正相关. 提出尽量少地提及原假消息、应解释原假消息错误的原因等6条辟谣建议，为社交网络假消息辟谣提供指导.

关键词： 假消息, 辟谣机理, 情感分析, 社交网络, 微博

Tab.1 Common false information classification table

Fig.1 Examples of refuting Weibo

Fig.2 Architecture of exploration on mechanism of correction

Tab.2 Data format of post

Tab.3 Data format of comment

Tab.4 Summary on influencing factors

Tab.5 Description of labeling

Tab.6 Data format of corpus

Tab.7 Process of data pre-processing

Fig.3 Comparison of model indicator

Tab.8 Examples of unrelated comments

Fig.4 Process of sentiment classification

Tab.9 Data category of predefined influencing factors

Tab.10 Selected methods for relation analysis

Tab.11 Results of difference analysis

Tab.12 Results of Spearman correlation analysis

Tab.13 Frequency distributions of posts whether contains warnings of false information in text，graphic or either format，respectively

Fig.5 Distribution of verified users

Fig.6 Comparison of effectives between posts explaining or not

Fig.7 Relation between proportion of original false content, source of information and effect of correction respectively

Fig.8 Combined effect of proportion of original false content and influence of source on effect of correction

Fig.9 Temporal changes on effect of correction, number of corrections and influence of source respectively


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