Characterizing Topics in Social Media Using Dynamics of Conversation
Online social media provides massive open-ended platforms for users of a wide variety of backgrounds, interests, and beliefs to interact and debate, facilitating countless discussions across a myriad of subjects. With numerous unique voices being lent to the ever-growing information stream, it is es...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/23/12/1642 |
| _version_ | 1797504900184145920 |
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| author | James Flamino Bowen Gong Frederick Buchanan Boleslaw K. Szymanski |
| author_facet | James Flamino Bowen Gong Frederick Buchanan Boleslaw K. Szymanski |
| author_sort | James Flamino |
| collection | DOAJ |
| description | Online social media provides massive open-ended platforms for users of a wide variety of backgrounds, interests, and beliefs to interact and debate, facilitating countless discussions across a myriad of subjects. With numerous unique voices being lent to the ever-growing information stream, it is essential to consider how the types of conversations that result from a social media post represent the post itself. We hypothesize that the biases and predispositions of users cause them to react to different topics in different ways not necessarily entirely intended by the sender. In this paper, we introduce a set of unique features that capture patterns of discourse, allowing us to empirically explore the relationship between a topic and the conversations it induces. Utilizing “microscopic” trends to describe “macroscopic” phenomena, we set a paradigm for analyzing information dissemination through the user reactions that arise from a topic, eliminating the need to analyze the involved text of the discussions. Using a Reddit dataset, we find that our features not only enable classifiers to accurately distinguish between content genre, but also can identify more subtle semantic differences in content under a single topic as well as isolating outliers whose subject matter is substantially different from the norm. |
| first_indexed | 2024-03-10T04:10:58Z |
| format | Article |
| id | doaj.art-413e88430c114f0b9e377ce7d330e92a |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T04:10:58Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-413e88430c114f0b9e377ce7d330e92a2023-11-23T08:11:02ZengMDPI AGEntropy1099-43002021-12-012312164210.3390/e23121642Characterizing Topics in Social Media Using Dynamics of ConversationJames Flamino0Bowen Gong1Frederick Buchanan2Boleslaw K. Szymanski3Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USADepartment of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USADepartment of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USADepartment of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USAOnline social media provides massive open-ended platforms for users of a wide variety of backgrounds, interests, and beliefs to interact and debate, facilitating countless discussions across a myriad of subjects. With numerous unique voices being lent to the ever-growing information stream, it is essential to consider how the types of conversations that result from a social media post represent the post itself. We hypothesize that the biases and predispositions of users cause them to react to different topics in different ways not necessarily entirely intended by the sender. In this paper, we introduce a set of unique features that capture patterns of discourse, allowing us to empirically explore the relationship between a topic and the conversations it induces. Utilizing “microscopic” trends to describe “macroscopic” phenomena, we set a paradigm for analyzing information dissemination through the user reactions that arise from a topic, eliminating the need to analyze the involved text of the discussions. Using a Reddit dataset, we find that our features not only enable classifiers to accurately distinguish between content genre, but also can identify more subtle semantic differences in content under a single topic as well as isolating outliers whose subject matter is substantially different from the norm.https://www.mdpi.com/1099-4300/23/12/1642information dynamicstopic modelingsemantic analysisnetwork entropy |
| spellingShingle | James Flamino Bowen Gong Frederick Buchanan Boleslaw K. Szymanski Characterizing Topics in Social Media Using Dynamics of Conversation Entropy information dynamics topic modeling semantic analysis network entropy |
| title | Characterizing Topics in Social Media Using Dynamics of Conversation |
| title_full | Characterizing Topics in Social Media Using Dynamics of Conversation |
| title_fullStr | Characterizing Topics in Social Media Using Dynamics of Conversation |
| title_full_unstemmed | Characterizing Topics in Social Media Using Dynamics of Conversation |
| title_short | Characterizing Topics in Social Media Using Dynamics of Conversation |
| title_sort | characterizing topics in social media using dynamics of conversation |
| topic | information dynamics topic modeling semantic analysis network entropy |
| url | https://www.mdpi.com/1099-4300/23/12/1642 |
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