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Enthusiasm and support: alternative sentiment classification for social movements on social media

Published: 23 June 2014 Publication History

Abstract

We present a novel sentiment classifier particularly designed for modeling and analyzing social movements; capturing levels of support (supportive versus non-supportive) and degrees of enthusiasm (enthusiastic versus passive). The resulting computational solution can help organizations involved with social causes to disseminate messages in a more informed and effective fashion; potentially leading to greater impact. Our findings suggest that enthusiastic and supportive tweets are more prevalent in tweets about social causes than other types of tweets on Twitter.

Reference

[1]
Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, P. K. (2010). Measuring User Influence in Twitter: The Million Follower Fallacy. ICWSM, 10, 10--17. Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011, February). Everyone's an influencer: quantifying influence on twitter. In Proceedings of the fourth ACM international conference on Web search and data mining (pp. 65--74). ACM. Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60(11), 2169--2188. Tinati, R., Carr, L., Hall, W., & Bentwood, J. (2012, April). Identifying communicator roles in twitter. In Proceedings of the 21st international conference companion on World Wide Web (pp. 1161--1168). ACM Sannella, M. J. 1994. Constraint Satisfaction and Debugging for Interactive User Interfaces. Doctoral Thesis. UMI Order Number: UMI Order No. GAX95-09398., University of Washington. Pang, B., Lee, L., & Vaithyanathan, S. (2002, July). Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10 (pp. 79--86). Association for Computational Linguistics.

Cited By

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  • (2022)Information Extraction from Social MediaProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557503(5148-5151)Online publication date: 17-Oct-2022
  • (2022)Information Extraction from Social Media: A Hands-On Tutorial on Tasks, Data, and Open Source ToolsAdvances in Information Retrieval10.1007/978-3-030-99739-7_74(589-596)Online publication date: 10-Apr-2022
  • (2021)Behaviour Anomaly Detection With Similarity-Based Sampling for Imbalanced DataData Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance10.4018/978-1-7998-7371-6.ch010(177-194)Online publication date: 2021
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  1. Enthusiasm and support: alternative sentiment classification for social movements on social media

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    cover image ACM Conferences
    WebSci '14: Proceedings of the 2014 ACM conference on Web science
    June 2014
    318 pages
    ISBN:9781450326223
    DOI:10.1145/2615569
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 23 June 2014

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    Author Tags

    1. data classification
    2. data corpus
    3. human factors
    4. social causes
    5. social network analysis

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    WebSci '14
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    WebSci '14: ACM Web Science Conference
    June 23 - 26, 2014
    Indiana, Bloomington, USA

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    WebSci '14 Paper Acceptance Rate 29 of 144 submissions, 20%;
    Overall Acceptance Rate 245 of 933 submissions, 26%

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    Cited By

    View all
    • (2022)Information Extraction from Social MediaProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557503(5148-5151)Online publication date: 17-Oct-2022
    • (2022)Information Extraction from Social Media: A Hands-On Tutorial on Tasks, Data, and Open Source ToolsAdvances in Information Retrieval10.1007/978-3-030-99739-7_74(589-596)Online publication date: 10-Apr-2022
    • (2021)Behaviour Anomaly Detection With Similarity-Based Sampling for Imbalanced DataData Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance10.4018/978-1-7998-7371-6.ch010(177-194)Online publication date: 2021
    • (2021)Exploring Multi-Task Multi-Lingual Learning of Transformer Models for Hate Speech and Offensive Speech Identification in Social MediaSN Computer Science10.1007/s42979-021-00455-52:2Online publication date: 4-Feb-2021
    • (2020)ALBERT-based fine-tuning model for cyberbullying analysisMultimedia Systems10.1007/s00530-020-00690-528:6(1941-1949)Online publication date: 18-Sep-2020
    • (2019)Capturing Signals of Enthusiasm and Support Towards Social Issues from TwitterProceedings of the 5th International Workshop on Social Media World Sensors10.1145/3345645.3351104(19-24)Online publication date: 12-Sep-2019
    • (2019)Multi-dataset-multi-task Neural Sequence Tagging for Information Extraction from TweetsProceedings of the 30th ACM Conference on Hypertext and Social Media10.1145/3342220.3344929(283-284)Online publication date: 12-Sep-2019
    • (2018)Detecting the Correlation between Sentiment and User-level as well as Text-Level Meta-data from Benchmark CorporaProceedings of the 29th on Hypertext and Social Media10.1145/3209542.3209562(2-10)Online publication date: 3-Jul-2018
    • (undefined)Who is Most Likely to Oppose Federal Tuition-Free College Policies? Investigating Variable Interactions of Sentiments to America’s College PromiseSSRN Electronic Journal10.2139/ssrn.3423054

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