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The Effect of Increasing Number of Topics to Polarization and Echo Chambers on SNS
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- Nagura Takuya
- Degree Programs in System and Information Engineering, University of Tsukuba
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- Akiyama Eizo
- Institute of Systems and Information Engineering University of Tsukuba
Bibliographic Information
- Other Title
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- SNSにおけるトピックス数の増加が意見の分極化とエコーチェンバーに与える影響
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Description
<p>Communication through SNS (Social Networking Services) and function of SNS platform can cause two phenomena polarization and echo chambers. In previous study [sasahara 21] about the communication on SNSs regarding a single topic, it has been shown that there is a correlation between these two phenomena and also that SNS functions facilitate the occurrence of these phenomena. In this study, focusing on the fact that real SNS users often deal with multiple topics on a daily basis, we analyzed an agent-based model of SNS communication regarding multiple topics. The analysis revealed that polarization of opinions occurs even when the number of topics increases but that echo chambers are less likely to occur as the number of topics increases. We have also found that there is little correlation between polarization and echo chamber under the condition with more than certain number of topics. Additionally, some functions of SNS such as user recommendations and reposts was found to facilitate echo chambers regardless of the number of topics. This result implies that the findings of previous research are robust even in multiple topics environment. These results suggest that topic diversity and function of SNS platform amendment may be a means of reducing the occurrence of echo chambers. In this model, we cannot take into account the impact of an individual’s interest in a specific topic on such as unfollowing behavior. In future research, we should expand our model to implement these user’s real behavior.</p>
Journal
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- Transactions of the Japanese Society for Artificial Intelligence
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Transactions of the Japanese Society for Artificial Intelligence 38 (4), B-N11_1-9, 2023-07-01
The Japanese Society for Artificial Intelligence
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Details 詳細情報について
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- CRID
- 1390859616450856448
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- ISSN
- 13468030
- 13460714
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- Text Lang
- ja
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- Article Type
- journal article
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- Data Source
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- JaLC
- Crossref
- KAKEN
- OpenAIRE
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- Abstract License Flag
- Disallowed