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An Evaluation Method for Content Analysis Based on Twitter Content Influence

Euijong Lee

Department of Computer Science and Engineering, Korea University, Seoul 136-713, Republic of Korea

E-mail Address: kongjjagae@korea.ac.kr

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Young-Gab Kim

Department of Computer and Information Security, Sejong University, Seoul 143-747, Republic of Korea

E-mail Address: alwaysgabi@sejong.ac.kr

Corresponding authors.

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Young-Duk Seo

Department of Computer Science and Engineering, Korea University, Seoul 136-713, Republic of Korea

E-mail Address: seoyoungd@korea.ac.kr

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Kwangsoo Seol

Department of Computer Science and Engineering, Korea University, Seoul 136-713, Republic of Korea

E-mail Address: seolks@korea.ac.kr

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, and

Doo-Kwon Baik

Department of Computer Science and Engineering, Korea University, Seoul 136-713, Republic of Korea

E-mail Address: baikdk@korea.ac.kr

Corresponding authors.

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https://doi.org/10.1142/S0218194017500310Cited by:3 (Source: Crossref)

Abstract

Twitter is a microblogging website, which has different characteristics from any other social networking service (SNS) in that it has one-directional relationships between users with short posts of less than 140 characters. These characteristics make Twitter not only a social network but also a news media. In addition, Twitter posts have been used and analyzed in various fields such as marketing, prediction of presidential elections, and requirement analysis. With an increase in Twitter usage, we need a more effective method to analyze Twitter content. In this paper, we propose a method for content analysis based on the influence of Twitter content. For measuring Twitter influence, we use the number of followers of the content author, retweet count, and currency of time. We perform experiments to compare the proposed method, frequency, numerical statistics, user influence, and sentiment score. The results show that the proposed method is slightly better than the other methods. In addition, we discuss Twitter characteristics and a method for an effective analysis of Twitter content.

Keywords:

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