An analysis and prediction model of outsiders percentage as a new popularity metric on Instagram
In this research, a new Instagram popularity metric was defined, i.e. outsiders percentage (OP) of a post. Outsiders are non-followers who liked a user’s post. It was found that OP is the most effective metric if compared to engagement rate and followers growth. Regression models were tested for pre...
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Elsevier
2020-09-01
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| Series: | ICT Express |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405959520300813 |
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doaj-936e1b329c3b429aae974e615f31f87b2020-11-25T03:53:49ZengElsevierICT Express2405-95952020-09-0163243248An analysis and prediction model of outsiders percentage as a new popularity metric on InstagramKristo Radion Purba0David Asirvatham1Raja Kumar Murugesan2Corresponding author.; School of Computing and IT, Taylor’s University, MalaysiaSchool of Computing and IT, Taylor’s University, MalaysiaSchool of Computing and IT, Taylor’s University, MalaysiaIn this research, a new Instagram popularity metric was defined, i.e. outsiders percentage (OP) of a post. Outsiders are non-followers who liked a user’s post. It was found that OP is the most effective metric if compared to engagement rate and followers growth. Regression models were tested for predicting OP, using features from user data, post data, hashtag, engagement, and image sentiment. The prediction accuracy (R2), reached up to 71.9% using Random Forest. This research also analyzed the trend of each feature against the OP. It was found that hashtag usage is the most important factor in raising OP.http://www.sciencedirect.com/science/article/pii/S2405959520300813Social mediaInstagramPopularityRegressionMachine learning |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Kristo Radion Purba David Asirvatham Raja Kumar Murugesan |
| spellingShingle |
Kristo Radion Purba David Asirvatham Raja Kumar Murugesan An analysis and prediction model of outsiders percentage as a new popularity metric on Instagram ICT Express Social media Popularity Regression Machine learning |
| author_facet |
Kristo Radion Purba David Asirvatham Raja Kumar Murugesan |
| author_sort |
Kristo Radion Purba |
| title |
An analysis and prediction model of outsiders percentage as a new popularity metric on Instagram |
| title_short |
An analysis and prediction model of outsiders percentage as a new popularity metric on Instagram |
| title_full |
An analysis and prediction model of outsiders percentage as a new popularity metric on Instagram |
| title_fullStr |
An analysis and prediction model of outsiders percentage as a new popularity metric on Instagram |
| title_full_unstemmed |
An analysis and prediction model of outsiders percentage as a new popularity metric on Instagram |
| title_sort |
analysis and prediction model of outsiders percentage as a new popularity metric on instagram |
| publisher |
Elsevier |
| series |
ICT Express |
| issn |
2405-9595 |
| publishDate |
2020-09-01 |
| description |
In this research, a new Instagram popularity metric was defined, i.e. outsiders percentage (OP) of a post. Outsiders are non-followers who liked a user’s post. It was found that OP is the most effective metric if compared to engagement rate and followers growth. Regression models were tested for predicting OP, using features from user data, post data, hashtag, engagement, and image sentiment. The prediction accuracy (R2), reached up to 71.9% using Random Forest. This research also analyzed the trend of each feature against the OP. It was found that hashtag usage is the most important factor in raising OP. |
| topic |
Social media Popularity Regression Machine learning |
| url |
http://www.sciencedirect.com/science/article/pii/S2405959520300813 |
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