Identifying Topic-based Opinion Leaders in Social Networks by Content and User Information

Keywords: social network analysis, opinion leader detection, flow of influence, PageRank algorithm, semantic kernels

Abstract

Social media is like a revolution since it has changed many things in people’s lifestyles by bringing new trends in communication, shopping, working…etc. In microblogging sites of the social media, more and more users meet every day, a wide range of ideas are quickly emerging, spreading, and creating interactive environments. In this context, social media can be seen as one of the most important sources of information affecting public opinion. Being inspired by the importance of social media, we propose Opinion Leader Detection (OLED) system in this paper. OLED has two main parts. A network was created by collecting users' information and their tweets shared within a specific time. In the first part, the tweets were labeled with their categories by semantic kernels for topic-based analysis. After the category information is obtained, the second part is attempt to detect whether the users are opinion leaders in their category. Then, the leadership scores were calculated with the formula generated and opinion leaders were determined in each category. We performed our experiments on a data collection gathered from Twitter that includes 17,234,924 tweets and 38,727 users. According to topic modeling and user modeling results, we give leadership scores to each user in the network. Users with highest scores are stated as opinion leaders. In order to evaluate OLED’s performance we also run PageRank algorithm on the same dataset. The experimental results show that our framework OLED generates remarkable performance in compare to PageRank algorithm nearly in all topics and all selected topN opinion leaders.

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Published
2020-12-30
How to Cite
[1]
A. Altinel, M. Hakkoz, E. Bozdag, and M. Ganiz, “Identifying Topic-based Opinion Leaders in Social Networks by Content and User Information”, IJISAE, vol. 8, no. 4, pp. 214-220, Dec. 2020.
Section
Research Article