Utilizing Structural Network Positions to Diversify People Recommendations on Twitter
Social recommender systems, such as “Who to follow” on Twitter, utilize approaches that recommend friends of a friend or interest-wise similar people. Such algorithmic approaches have been criticized for resulting in filter bubbles and echo chambers, calling for diversity-enhancing recommendation st...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Advances in Human-Computer Interaction |
| Online Access: | http://dx.doi.org/10.1155/2022/6584394 |
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| _version_ | 1832568457526247424 |
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| author | Ekaterina Olshannikova Erjon Skenderi Thomas Olsson Sami Koivunen Jukka Huhtamäki |
| author_facet | Ekaterina Olshannikova Erjon Skenderi Thomas Olsson Sami Koivunen Jukka Huhtamäki |
| author_sort | Ekaterina Olshannikova |
| collection | DOAJ |
| description | Social recommender systems, such as “Who to follow” on Twitter, utilize approaches that recommend friends of a friend or interest-wise similar people. Such algorithmic approaches have been criticized for resulting in filter bubbles and echo chambers, calling for diversity-enhancing recommendation strategies. Consequently, this article proposes a social diversification strategy for recommending potentially relevant people based on three structural positions in egocentric networks: dormant ties, mentions of mentions, and community membership. In addition to describing our analytical approach, we report an experiment with 39 Twitter users who evaluated 72 recommendations from each proposed network structural position altogether. The users were able to identify relevant connections from all recommendation groups. Yet, perceived familiarity had a strong effect on perceptions of relevance and willingness to follow-up on the recommendations. The proposed strategy contributes to the design of a people recommender system, which exposes users to diverse recommendations and facilitates new social ties in online social networks. In addition, we advance user-centered evaluation methods by proposing measures for subjective perceptions of people recommendations. |
| format | Article |
| id | doaj-art-c325e5b6575d42ea8b659c321f39e3af |
| institution | Kabale University |
| issn | 1687-5907 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Human-Computer Interaction |
| spelling | doaj-art-c325e5b6575d42ea8b659c321f39e3af2025-02-03T00:59:06ZengWileyAdvances in Human-Computer Interaction1687-59072022-01-01202210.1155/2022/6584394Utilizing Structural Network Positions to Diversify People Recommendations on TwitterEkaterina Olshannikova0Erjon Skenderi1Thomas Olsson2Sami Koivunen3Jukka Huhtamäki4Faculty of Information Technology and Communication StudiesDepartment of Industrial and Information ManagementFaculty of Information Technology and Communication StudiesFaculty of Information Technology and Communication StudiesDepartment of Industrial and Information ManagementSocial recommender systems, such as “Who to follow” on Twitter, utilize approaches that recommend friends of a friend or interest-wise similar people. Such algorithmic approaches have been criticized for resulting in filter bubbles and echo chambers, calling for diversity-enhancing recommendation strategies. Consequently, this article proposes a social diversification strategy for recommending potentially relevant people based on three structural positions in egocentric networks: dormant ties, mentions of mentions, and community membership. In addition to describing our analytical approach, we report an experiment with 39 Twitter users who evaluated 72 recommendations from each proposed network structural position altogether. The users were able to identify relevant connections from all recommendation groups. Yet, perceived familiarity had a strong effect on perceptions of relevance and willingness to follow-up on the recommendations. The proposed strategy contributes to the design of a people recommender system, which exposes users to diverse recommendations and facilitates new social ties in online social networks. In addition, we advance user-centered evaluation methods by proposing measures for subjective perceptions of people recommendations.http://dx.doi.org/10.1155/2022/6584394 |
| spellingShingle | Ekaterina Olshannikova Erjon Skenderi Thomas Olsson Sami Koivunen Jukka Huhtamäki Utilizing Structural Network Positions to Diversify People Recommendations on Twitter Advances in Human-Computer Interaction |
| title | Utilizing Structural Network Positions to Diversify People Recommendations on Twitter |
| title_full | Utilizing Structural Network Positions to Diversify People Recommendations on Twitter |
| title_fullStr | Utilizing Structural Network Positions to Diversify People Recommendations on Twitter |
| title_full_unstemmed | Utilizing Structural Network Positions to Diversify People Recommendations on Twitter |
| title_short | Utilizing Structural Network Positions to Diversify People Recommendations on Twitter |
| title_sort | utilizing structural network positions to diversify people recommendations on twitter |
| url | http://dx.doi.org/10.1155/2022/6584394 |
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