research-article

Sentiment-driven Community Profiling and Detection on Social Media

Authors:

Hasan DavulcuAuthors Info & Claims

HT '18: Proceedings of the 29th on Hypertext and Social Media

Pages 229 - 237

https://doi.org/10.1145/3209542.3209565

Published: 03 July 2018 Publication History

Abstract

Web 2.0 helps to expand the range and depth of conversation on many issues and facilitates the formation of online communities. Online communities draw various individuals together based on their common opinions on a core set of issues. Most existing community detection methods merely focus on discovering communities without providing any insight regarding the collective opinions of community members and the motives behind the formation of communities. Several efforts have been made to tackle this problem by presenting a set of keywords as a community profile. However, they neglect the positions of community members towards keywords, which play an important role for understanding communities in the highly polarized atmosphere of social media. To this end, we present a sentiment-driven community profiling and detection framework which aims to provide community profiles presenting positive and negative collective opinions of community members separately. With this regard, our framework initially extracts key expressions in users' messages as representative of issues and then identifies users' positive/negative attitudes towards these key expressions. Next, it uncovers a low-dimensional latent space in order to cluster users according to their opinions and social interactions (i.e., retweets). We demonstrate the effectiveness of our framework through quantitative and qualitative evaluations.

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Cited By

Nayak BHota PMishra S(2024)IGSentiment Analysis of Russia and Ukraine War on Twitter Data: Using Azure Machin Learning and Deep LearningPervasive Knowledge and Collective Intelligence on Web and Social Media10.1007/978-3-031-66044-3_8(110-119)Online publication date: 13-Aug-2024
https://doi.org/10.1007/978-3-031-66044-3_8
Tjandra BLuhukay DSyahchari D(2023)The Effect of Using Hashtags on Consumer Engagement with The Promotion of Property Products2023 International Conference on Informatics, Multimedia, Cyber and Informations System (ICIMCIS)10.1109/ICIMCIS60089.2023.10349084(61-66)Online publication date: 7-Nov-2023
https://doi.org/10.1109/ICIMCIS60089.2023.10349084
Logan ALaCasse PLunday B(2023)Social network analysis of Twitter interactions: a directed multilayer network approachSocial Network Analysis and Mining10.1007/s13278-023-01063-213:1Online publication date: 7-Apr-2023
https://doi.org/10.1007/s13278-023-01063-2
Show More Cited By

Index Terms

Sentiment-driven Community Profiling and Detection on Social Media
1. Networks
  1. Network types
    1. Overlay and other logical network structures

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Published In

cover image ACM Conferences

HT '18: Proceedings of the 29th on Hypertext and Social Media

July 2018

266 pages

ISBN:9781450354271

DOI:10.1145/3209542

General Chair:
Dongwon Lee
Penn State University, USA
,
Program Chairs:
Nishanth Sastry
King's College London, UK
,
Ingmar Weber
QCRI, Qatar

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SIGCHI: Specialist Interest Group in Computer-Human Interaction of the ACM

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Association for Computing Machinery

New York, NY, United States

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Published: 03 July 2018

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HT '18: 29th ACM Conference on Hypertext and Social Media

July 9 - 12, 2018

MD, Baltimore, USA

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HT '18 Paper Acceptance Rate 19 of 69 submissions, 28%;

Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

Nayak BHota PMishra S(2024)IGSentiment Analysis of Russia and Ukraine War on Twitter Data: Using Azure Machin Learning and Deep LearningPervasive Knowledge and Collective Intelligence on Web and Social Media10.1007/978-3-031-66044-3_8(110-119)Online publication date: 13-Aug-2024
https://doi.org/10.1007/978-3-031-66044-3_8
Tjandra BLuhukay DSyahchari D(2023)The Effect of Using Hashtags on Consumer Engagement with The Promotion of Property Products2023 International Conference on Informatics, Multimedia, Cyber and Informations System (ICIMCIS)10.1109/ICIMCIS60089.2023.10349084(61-66)Online publication date: 7-Nov-2023
https://doi.org/10.1109/ICIMCIS60089.2023.10349084
Logan ALaCasse PLunday B(2023)Social network analysis of Twitter interactions: a directed multilayer network approachSocial Network Analysis and Mining10.1007/s13278-023-01063-213:1Online publication date: 7-Apr-2023
https://doi.org/10.1007/s13278-023-01063-2
Atzenbeck CNürnberg PRoßner D(2021)Synthesising augmentation and automationNew Review of Hypermedia and Multimedia10.1080/13614568.2021.194223727:1-2(177-203)Online publication date: 1-Jul-2021
https://doi.org/10.1080/13614568.2021.1942237
Salehi ADavulcu H(2020)Graph Attention Auto-Encoders2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI50040.2020.00154(989-996)Online publication date: Nov-2020
https://doi.org/10.1109/ICTAI50040.2020.00154
Atzenbeck CNürnberg PAtzenbeck CRubart JMillard D(2019)Hypertext as MethodProceedings of the 30th ACM Conference on Hypertext and Social Media10.1145/3342220.3343669(29-38)Online publication date: 12-Sep-2019
https://dl.acm.org/doi/10.1145/3342220.3343669
Wang LNiu JLiu XMao K(2019)The Silent Majority Speaks: Inferring Silent Users' Opinions in Online Social NetworksThe World Wide Web Conference10.1145/3308558.3313423(3321-3327)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313423

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