Estimating community feedback effect on topic choice in social media with predictive modeling
<p dir="ltr">Social media users post content on various topics. A defining feature of social media is that other users can provide feedback—called community feedback—to their content in the form of comments, replies, and retweets. We hypothesize that the amount of received feedback i...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , |
| منشور في: |
2020
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513512305328128 |
|---|---|
| author | David Ifeoluwa Adelani (18877216) |
| author2 | Ryota Kobayashi (1427098) Ingmar Weber (149886) Przemyslaw A. Grabowicz (18877219) |
| author2_role | author author author |
| author_facet | David Ifeoluwa Adelani (18877216) Ryota Kobayashi (1427098) Ingmar Weber (149886) Przemyslaw A. Grabowicz (18877219) |
| author_role | author |
| dc.creator.none.fl_str_mv | David Ifeoluwa Adelani (18877216) Ryota Kobayashi (1427098) Ingmar Weber (149886) Przemyslaw A. Grabowicz (18877219) |
| dc.date.none.fl_str_mv | 2020-08-31T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1140/epjds/s13688-020-00243-w |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Estimating_community_feedback_effect_on_topic_choice_in_social_media_with_predictive_modeling/26095102 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Data management and data science Human-centred computing Social feedback Social influence User behavior modeling |
| dc.title.none.fl_str_mv | Estimating community feedback effect on topic choice in social media with predictive modeling |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Social media users post content on various topics. A defining feature of social media is that other users can provide feedback—called community feedback—to their content in the form of comments, replies, and retweets. We hypothesize that the amount of received feedback influences the choice of topics on which a social media user posts. However, it is challenging to test this hypothesis as user heterogeneity and external confounders complicate measuring the feedback effect. Here, we investigate this hypothesis with a predictive approach based on an interpretable model of an author’s decision to continue the topic of their previous post. We explore the confounding factors, including author’s topic preferences and unobserved external factors such as news and social events, by optimizing the predictive accuracy. This approach enables us to identify which users are susceptible to community feedback. Overall, we find that 33% and 14% of active users in Reddit and Twitter, respectively, are influenced by community feedback. The model suggests that this feedback alters the probability of topic continuation up to 14%, depending on the user and the amount of feedback.</p><h2>Other Information</h2><p dir="ltr">Published in: EPJ Data Science<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1140/epjds/s13688-020-00243-w" target="_blank">https://dx.doi.org/10.1140/epjds/s13688-020-00243-w</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_bf6dfaa5d52f582f061f793d4e651d4a |
| identifier_str_mv | 10.1140/epjds/s13688-020-00243-w |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26095102 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Estimating community feedback effect on topic choice in social media with predictive modelingDavid Ifeoluwa Adelani (18877216)Ryota Kobayashi (1427098)Ingmar Weber (149886)Przemyslaw A. Grabowicz (18877219)Information and computing sciencesData management and data scienceHuman-centred computingSocial feedbackSocial influenceUser behavior modeling<p dir="ltr">Social media users post content on various topics. A defining feature of social media is that other users can provide feedback—called community feedback—to their content in the form of comments, replies, and retweets. We hypothesize that the amount of received feedback influences the choice of topics on which a social media user posts. However, it is challenging to test this hypothesis as user heterogeneity and external confounders complicate measuring the feedback effect. Here, we investigate this hypothesis with a predictive approach based on an interpretable model of an author’s decision to continue the topic of their previous post. We explore the confounding factors, including author’s topic preferences and unobserved external factors such as news and social events, by optimizing the predictive accuracy. This approach enables us to identify which users are susceptible to community feedback. Overall, we find that 33% and 14% of active users in Reddit and Twitter, respectively, are influenced by community feedback. The model suggests that this feedback alters the probability of topic continuation up to 14%, depending on the user and the amount of feedback.</p><h2>Other Information</h2><p dir="ltr">Published in: EPJ Data Science<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1140/epjds/s13688-020-00243-w" target="_blank">https://dx.doi.org/10.1140/epjds/s13688-020-00243-w</a></p>2020-08-31T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1140/epjds/s13688-020-00243-whttps://figshare.com/articles/journal_contribution/Estimating_community_feedback_effect_on_topic_choice_in_social_media_with_predictive_modeling/26095102CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/260951022020-08-31T09:00:00Z |
| spellingShingle | Estimating community feedback effect on topic choice in social media with predictive modeling David Ifeoluwa Adelani (18877216) Information and computing sciences Data management and data science Human-centred computing Social feedback Social influence User behavior modeling |
| status_str | publishedVersion |
| title | Estimating community feedback effect on topic choice in social media with predictive modeling |
| title_full | Estimating community feedback effect on topic choice in social media with predictive modeling |
| title_fullStr | Estimating community feedback effect on topic choice in social media with predictive modeling |
| title_full_unstemmed | Estimating community feedback effect on topic choice in social media with predictive modeling |
| title_short | Estimating community feedback effect on topic choice in social media with predictive modeling |
| title_sort | Estimating community feedback effect on topic choice in social media with predictive modeling |
| topic | Information and computing sciences Data management and data science Human-centred computing Social feedback Social influence User behavior modeling |