Abstract
In this paper, we present a novel method for quantifying a user's ability to spread information based on the number of Retweets (RTs) and Likes they receive on Twitter. In today's social network services, there are numerous users with the ability to spread information, called “influencers”. However, even if they post the same content, the reactions they receive vary from user to user. Therefore, it is useful to create an index that represents the diffusion ability of each account to analyze diffusion behavior in social network services. In general, the diffusion status of information on Twitter is often quantified in terms of the number of RTs, Likes, and impressions of tweets alone. In this novel method, we propose a method for extracting indicators that show the diffusion power, not of tweets alone, but users as a unit, by measuring the ratio of the number of RTs and Likes based on the number of RTs and Likes of users in the past. The index obtained by this method can be used as an indicator for analyzing diffusion behavior on Twitter and may help conduct a more granular analysis. In this study, we conducted an experiment in which we collected tweets from 10 international celebrities for three years, divided them into multiple time series types, and applied this method to qualitatively evaluate them from the tweet text. The results showed that a bias exists when the period covered by the method is narrow, but when measured over a periodic unit of one year, there was no significant blurring, and it was possible to determine the status of the user in terms of the tweet text. We also found that each field was coherent and that there was a nature to the field.
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This research is a product of the research program of The Tokyo Foundation for Policy Research. We would like to thank Editage (www.editage.com) for English language editing.
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Noji, Y., Okada, R., Nakanishi, T. (2023). Represent Score as the Measurement of User Influence on Twitter. In: Lee, R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. SNPD 2022. Studies in Computational Intelligence, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-031-19604-1_3
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