Abstract
In social media, people often press a "Like" button to indicate their shared interest in a particular content or to acknowledge the user who posted the content. Such activities form relationships and networks among people, raising interesting questions about their unique characteristics and implications. However, little research has investigated such Likes as a main study focus. To address this lack of understanding, based on a theoretical framework, we present an analysis of the structural, influential, and contextual aspects of Like activities from the test datasets of 20 million users and their 2 billion Like activities in Instagram. Our study results first highlight that Like activities and networks increase exponentially, and are formed and developed by one's friends and many random users. Second, we observe that five other essential Instagram elements influence the number of Likes to different extents, but following others will not necessarily increase the number of Likes that one receives. Third, we explore the relationship between LDA-based topics and Likes, characterize two user groups-specialists and generalists- and show that specialists tend to receive more Likes and promote themselves more than generalists. We finally discuss theoretical and practical implications and future research directions.
| Original language | English (US) |
|---|---|
| Title of host publication | HT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 273-282 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781450333955 |
| DOIs | |
| State | Published - Aug 24 2015 |
| Event | 26th ACM Conference on Hypertext and Social Media, HT 2015 - Guzelyurt, Cyprus Duration: Sep 1 2015 → Sep 4 2015 |
Publication series
| Name | HT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media |
|---|
Other
| Other | 26th ACM Conference on Hypertext and Social Media, HT 2015 |
|---|---|
| Country | Cyprus |
| City | Guzelyurt |
| Period | 9/1/15 → 9/4/15 |
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Software
- Computer Graphics and Computer-Aided Design
- Human-Computer Interaction
Cite this
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No reciprocity in "liking" photos : Analyzing like activities in instagram. / Yea, Jin; Han, Jang Kyungsik; Lee, Dongwon.
HT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media. Association for Computing Machinery, Inc, 2015. p. 273-282 (HT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - No reciprocity in "liking" photos
T2 - Analyzing like activities in instagram
AU - Yea, Jin
AU - Han, Jang Kyungsik
AU - Lee, Dongwon
PY - 2015/8/24
Y1 - 2015/8/24
N2 - In social media, people often press a "Like" button to indicate their shared interest in a particular content or to acknowledge the user who posted the content. Such activities form relationships and networks among people, raising interesting questions about their unique characteristics and implications. However, little research has investigated such Likes as a main study focus. To address this lack of understanding, based on a theoretical framework, we present an analysis of the structural, influential, and contextual aspects of Like activities from the test datasets of 20 million users and their 2 billion Like activities in Instagram. Our study results first highlight that Like activities and networks increase exponentially, and are formed and developed by one's friends and many random users. Second, we observe that five other essential Instagram elements influence the number of Likes to different extents, but following others will not necessarily increase the number of Likes that one receives. Third, we explore the relationship between LDA-based topics and Likes, characterize two user groups-specialists and generalists- and show that specialists tend to receive more Likes and promote themselves more than generalists. We finally discuss theoretical and practical implications and future research directions.
AB - In social media, people often press a "Like" button to indicate their shared interest in a particular content or to acknowledge the user who posted the content. Such activities form relationships and networks among people, raising interesting questions about their unique characteristics and implications. However, little research has investigated such Likes as a main study focus. To address this lack of understanding, based on a theoretical framework, we present an analysis of the structural, influential, and contextual aspects of Like activities from the test datasets of 20 million users and their 2 billion Like activities in Instagram. Our study results first highlight that Like activities and networks increase exponentially, and are formed and developed by one's friends and many random users. Second, we observe that five other essential Instagram elements influence the number of Likes to different extents, but following others will not necessarily increase the number of Likes that one receives. Third, we explore the relationship between LDA-based topics and Likes, characterize two user groups-specialists and generalists- and show that specialists tend to receive more Likes and promote themselves more than generalists. We finally discuss theoretical and practical implications and future research directions.
UR - http://www.scopus.com/inward/record.url?scp=84956968935&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84956968935&partnerID=8YFLogxK
U2 - 10.1145/2700171.2791043
DO - 10.1145/2700171.2791043
M3 - Conference contribution
AN - SCOPUS:84956968935
T3 - HT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media
SP - 273
EP - 282
BT - HT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media
PB - Association for Computing Machinery, Inc
ER -