基于Twitter社交媒体社交网络分析的问题传播关键行为者检测

Audy Joize Oroh, Y. Bandung, Luqman Muhammad Zagi
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引用次数: 7

摘要

随着当今社会传播设施的发展,社交媒体成为向他人传递信息最有效、最高效的手段。社交媒体的优势最终导致了社交媒体的滥用,并导致了恶作剧和仇恨言论的出现和发展。像推特这样的在线社交媒体是网络空间中使用最广泛的交流手段。在Twitter上传播新闻的一个重要问题是,经常传播新闻的关键角色的存在,以及影响社交媒体的账户。这些账号通常有很多粉丝。发现关键行为者是处理Twitter上的仇恨言论和假新闻的障碍之一。采用度中心性、接近中心性、中间中心性和特征向量中心性方法的中心性分析算法来检测关键行为者。此外,情感值用于确定评论在账户帖子中的评论的正负值。通过对度中心性算法、中间中心性算法和特征向量中心性算法的分析,发现对问题传播影响最大、成为关键角色的用户是user_id为150589950的用户。情感分析算法得到推文数量所表示的情感计算结果。推文传播中最具影响力的用户可以从推文的数量中看出,从推文数量中可以找到。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of the Key Actor of Issues Spreading Based on Social Network Analysis in Twitter Social Media
With the development of today's society's communication facilities, social media becomes the most effective and efficient means of conveying information to other parties. Social media's advantages ultimately contribute to social media misuse and contribute to the emergence and development of hoaxes and hate speech. Online social media such as Twitter is the most widely used means of communication in cyberspace. The important issue with the spread of news on Twitter is the presence of key actors who often spread the issue and are accounts that influence social media. These accounts usually have a lot of followers. Detection of the key actors is one of the obstacles to handling hate speech and fake news on Twitter. It can be solved using a centrality analysis algorithm with degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality method to detect the key actor. Also, sentiment value is used to determine the positive or negative value of the comments' comments in the account post. The analysis of degree centrality algorithms, betweenness centrality, and eigenvector centrality has shown that the user who has the most influence and becomes a key actor in the spread of the issue is the user with user_id 150589950. The sentiment analysis algorithm obtained the sentiment calculation results shown by the tweet amount. The most influential users in the spread of tweets can be seen from the number of tweets that can be found from the tweet amount.
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