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Predicting the Future with Social Media
Open accessAbstract
In recent years, social media has become ubiquitous and important for social networking and content sharing. And yet, the content that is generated from these websites remains largely untapped. In this paper, we demonstrate how social media content can be used to predict real-world outcomes. In particular, we use the chatter from Twitter.com to forecast box-office revenues for movies. We show that a simple model built from the rate at which tweets are created about particular topics can outperform market-based predictors. We further demonstrate how sentiments extracted from Twitter can be further utilized to improve the forecasting power of social media.
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“Much research rests upon the assumption that online social traces reflect in some quantifiable way real-world phenomena (Asur and Huberman, 2010; Kıcıman and Richardson, 2015)”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Asur and Huberman, 2010
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Cites
Opinion Mining and Sentiment Analysis
Pang, B., & Lee, L. (2008) · now publishers, Inc. eBooks · doi:10.1561/9781601981516 · Cited by 6,957
Opinion mining and sentiment analysis
Pang, B., & Lee, L. (2008) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000011 · Cited by 6,372
Java, A., Song, X., Finin, T., & Tseng, B. L. (2007) · doi:10.1145/1348549.1348556 · Cited by 2,662
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
Snow, R., O'Connor, B., Jurafsky, D., & Ng, A. Y. (2008) · Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08 · doi:10.3115/1613715.1613751 · Cited by 1,887
Twitter power: Tweets as electronic word of mouth
Jansen, B. J. J., Zhang, M., Sobel, K., & Chowdury, A. (2009) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21149 · Cited by 1,766
Social networks that matter: Twitter under the microscope
Huberman, B. A., Romero, D. M., & Wu, F. (2008) · First Monday · doi:10.5210/fm.v14i1.2317 · Cited by 965
Self-citationLarge-Scale Sentiment Analysis for News and Blogs
Godbole, N., Srinivasaiah, M., & Skiena, S. (2007) · International Conference on Weblogs and Social Media · Cited by 581
The predictive power of online chatter
Gruhl, D., Guha, R. V., Kumar, R., Novak, J., & Tomkins, A. (2005) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1081870.1081883 · Cited by 411
The dynamics of viral marketing
Leskovec, J., Adamic, L. A., & Huberman, B. A. (2006) · ACM Conference on Electronic Commerce (EC) · doi:10.1145/1134707.1134732 · Cited by 391
Self-citationPredicting Movie Sales from Blogger Sentiment
Mishne, G., & Glance, N. (2006) · Cited by 354
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Roster of College of Chaplains.
Nann, S., Krauss, J. S., & Schoder, D. (1978) · PubMed · Cited by 40
PANAS-t: A Pychometric Scale for Measuring Sentiments on TwitterPreprint
Gonçalves, P., Benevenuto, F., & Cha, M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1308.1857 · Cited by 40
Boecking, B., Hall, M., & Schneider, J. (2015) · Policy & Internet · doi:10.1002/poi3.89 · Cited by 37
Crowd explicit sentiment analysis
Montejo‐Ráez, A., Díaz–Galiano, M. C., Martínez-Santiago, F. J., & López, L. A. U. (2014) · Knowledge-Based Systems · doi:10.1016/j.knosys.2014.05.007 · Cited by 39
Understanding Twitter as an e-WOM
Hodeghatta, U. R., & Sahney, S. (2016) · Journal of Systems and Information Technology · doi:10.1108/jsit-12-2014-0074 · Cited by 39
Can Twitter Proxy the Investors' Sentiment? The Case for the Technology Sector
Corea, F. (2016) · Big Data Research · doi:10.1016/j.bdr.2016.05.001 · Cited by 40
Using Twitter sentiment and emotions analysis of Google Trends for decisions making
D’Avanzo, E., Pilato, G., & Lytras, M. D. (2017) · Data Technologies and Applications · doi:10.1108/prog-02-2016-0015 · Cited by 39
Tales of emotion and stock in China: volatility, causality and prediction
Zhou, Z., Xu, K., & Zhao, J. (2017) · World Wide Web · doi:10.1007/s11280-017-0495-4 · Cited by 39
Crypto-currencies narrated on tweets: a sentiment analysis approach
Rouhani, S., & Abedin, E. (2019) · International Journal of Ethics and Systems · doi:10.1108/ijoes-12-2018-0185 · Cited by 48
Leveraging analytics to produce compelling and profitable film content
Behrens, R., Foutz, N. Z., Franklin, M., Funk, J., Gutierrez-Navratil, F., Hofmann, J., & Leibfried, U. (2021) · Journal of Cultural Economics · doi:10.1007/s10824-019-09372-1 · Cited by 39
A review of approaches for topic detection in Twitter
Mottaghinia, Z., Feizi‐Derakhshi, M., Farzinvash, L., & Salehpour, P. (2020) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2020.1785019 · Cited by 41
Box office sales and social media: A cross-platform comparison of predictive ability and mechanisms
Bogaert, M., Ballings, M., den Poel, D. V., & Oztekin, A. (2021) · Decision Support Systems · doi:10.1016/j.dss.2021.113517 · Cited by 39
The Social Media “Magic”: Virtually Engaging Visitors during COVID-19 Temporary Closures
Ryder, B. N., Zhang, T. C., & Hua, N. (2021) · Administrative Sciences · doi:10.3390/admsci11020053 · Cited by 39
Using Social Media to Predict Traffic Flow under Special Event Conditions
Ni, M. A. (2014) · ProQuest Demo Repository · Cited by 39
Social network sites: early adopters' personality and influence
Lynn, T., Muzellec, L., Caemmerer, B., & Turley, D. (2017) · Journal of Product & Brand Management · doi:10.1108/jpbm-10-2015-1025 · Cited by 38
Sentiment Analysis: an Application to Anadolu University
Öztürk, Z. K., Çiçek, Z. İ. E., & Aydın, Z. E. (2017) · Acta Physica Polonica A · doi:10.12693/aphyspola.132.753 · Cited by 38
Xu, J., Compton, R., Lu, T.-C., & Allen, D. (2014) · ACM Web Science Conference (WebSci) · doi:10.1145/2615569.2615694 · Cited by 38
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Lindner, A. M., Lindquist, M., & Arnold, J. R. (2015) · Sociological Inquiry · doi:10.1111/soin.12081 · Cited by 38
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Chen, R., & Xu, W. X. (2016) · Electronic Commerce Research · doi:10.1007/s10660-016-9243-6 · Cited by 38
Event Classification in Microblogs via Social Tracking
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Facebook drives behavior of passive households in stock markets
Siikanen, M., Baltakys, K., Kanniainen, J., Vatrapu, R., Mukkamala, R. R., & Hussain, A. (2018) · Finance research letters · doi:10.1016/j.frl.2018.03.020 · Cited by 38
The Conundrum of Success in Music: Playing it or Talking About it?
Cosimato, A., Prisco, R. D., Guarino, A., Malandrino, D., Lettieri, N., Sorrentino, G., & Zaccagnino, R. (2019) · IEEE Access · doi:10.1109/access.2019.2937743 · Cited by 40
Mohammed, T. A., Ghareeb, A. H., Al-bayaty, H., & Aljawarneh, S. A. (2019) · International Conference on Data Science, E-Learning and Information Systems (DATA) · doi:10.1145/3368691.3368717 · Cited by 37
RetractedBakar, M. S. A., & Bidin, R. B. H. (2014) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2014.04.065 · Cited by 37
Lehmann, B. A., Ruiter, R. A. C., & Kok, G. (2013) · BMC Public Health · doi:10.1186/1471-2458-13-547 · Cited by 37
Listen to me — Evaluating the influence of micro-blogs
Li, F., & DU, T. C.-T. (2014) · Decision Support Systems · doi:10.1016/j.dss.2014.03.008 · Cited by 38
Directional Prediction of Stock Prices Using Breaking News on Twitter
Alostad, H., & Davulcu, H. (2015) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat.2015.82 · Cited by 39
Activity Prediction: A Twitter-based Exploration
Weerkamp, W., & de Rijke, M. (2012) · UvA-DARE (University of Amsterdam) · Cited by 37
In data we trust? The implications of datafication for social monitoring
van Dijck, J. (2017) · Matrizes · doi:10.11606/issn.1982-8160.v11i1p39-59 · Cited by 37
Sentiment Analysis of Azerbaijani twits Using Logistic Regression, Naive Bayes and SVM
Hasanli, H., & Rustamov, S. (2019) · IEEE International Conference on Application of Information and Communication Technologies (AICT) · doi:10.1109/aict47866.2019.8981793 · Cited by 37
Bello-Orgaz, G., Mesas, R. M., Zarco, C., Rodríguez, V., Cordón, Ó., & Camacho, D. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102220 · Cited by 37
Online Social Network Analysis: A Survey of Research Applications in Computer SciencePreprint
Kurka, D. B., Godoy, A., & Zuben, F. J. V. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.05655 · Cited by 51
News Consumption during the Italian Referendum: A Cross-Platform Analysis on Facebook and Twitter
Vicario, M. D., Gaito, S., Quattrociocchi, W., Zignani, M., & Zollo, F. (2017) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2017.33 · Cited by 38
de Zúñiga, H. G., González-González, P., & Goyanes, M. (2022) · American Behavioral Scientist · doi:10.1177/00027642221118272 · Cited by 45
Park, J., Ciampaglia, G. L., & Ferrara, E. (2016) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2818048.2820065 · Cited by 36
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Carchiolo, V., Longheu, A., & Malgeri, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-22741-2_2 · Cited by 35
Hot topic detection in local areas using Twitter and Wikipedia
Ishikawa, S., Arakawa, Y., Tagashira, S., & Fukuda, K. (2012) · Automation, Robotics and Control Systems · Cited by 35
Knowledge management using social media: A comparative study between blogs and Facebook
Chan, R. C. H., Chu, S. K. W., Lee, C. W. Y., Chan, B. K. T., & Leung, C. K. (2013) · Proceedings of the American Society for Information Science and Technology · doi:10.1002/meet.14505001069 · Cited by 37
Mendes, P. N., Passant, A., & Kapanipathi, P. (2010) · International Conference on Semantic Systems · doi:10.1145/1839707.1839762 · Cited by 35
Microblog Retrieval Using Topical Features and Query Expansion.
Lau, C. H., Li, Y., & Tjondronegoro, D. (2011) · Cited by 35
Predicting the Future Using Web Knowledge: State of the Art Survey
Mosavi, A. H., Bathla, Y., & Várkonyi-Kóczy, A. R. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-67459-9_42 · Cited by 35
Mitchell, C., Dinkha, J., & Abdulhamid, A. (2018) · Contention · doi:10.3167/cont.2018.060105 · Cited by 35
Grid Search Optimization (GSO) Based Future Sales Prediction for Big Mart
Behera, G., & Nain, N. (2019) · International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) · doi:10.1109/sitis.2019.00038 · Cited by 35
Classification of Arabic Tweets: A Review
Alruily, M. (2021) · Electronics · doi:10.3390/electronics10101143 · Cited by 39
Evaggelos Spyrou, A., & Phivos Mylonas, B. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.12.104 · Cited by 35
Sharma, S., & Behl, R. (2022) · Psychological Studies · doi:10.1007/s12646-022-00675-6 · Cited by 45
Franklin, M., Searle, N., Russell, D. S., & Townley, B. (2013) · Creativity and Innovation Management · doi:10.1111/caim.12029 · Cited by 35
Big data and Wikipedia research: social science knowledge across disciplinary divides
Schroeder, R., & Taylor, L. (2015) · Information Communication & Society · doi:10.1080/1369118x.2015.1008538 · Cited by 35
Sentiment Analysis on Microblogging by Integrating Text and Image Features
Zhang, Y., Shang, L., & Jia, X. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-18032-8_5 · Cited by 35
Identification and Characterization of Events in Social Media
Becker, H. (2011) · doi:10.7916/d8vm4qvd · Cited by 34
Persuasive Technology for Smartphone Fitness Apps
Yoganathan, D., & Sangaralingam, K. (2013) · Pacific Asia Conference on Information Systems · Cited by 34
The royal birth of 2013: Analysing and visualising public sentiment in the UK using Twitter
Nguyen, V., Varghese, B., & Barker, A. (2013) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2013.6691669 · Cited by 33
A straw shows which way the wind blows
Yin, P., Luo, P., Wang, M., & Lee, W.-C. (2012) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2124295.2124370 · Cited by 34
Sharing cathartic stories online: The internet as a means of expression following a crisis event
Veer, E., Ozanne, L. K., & Hall, C. M. (2015) · Journal of Consumer Behaviour · doi:10.1002/cb.1569 · Cited by 34
Big Data Sentiment Analysis for Brand Monitoring in Social Media Streams by Cloud Computing
Benedetto, F., & Tedeschi, A. (2016) · Studies in computational intelligence · doi:10.1007/978-3-319-30319-2_14 · Cited by 34
YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles
Li, T., Lin, L., Choi, M., Fu, K., Gong, S., & Wang, J. (2018) · International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) · doi:10.1109/isai-nlp.2018.8692799 · Cited by 34
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A Survey on Machine Learning Techniques in Movie Revenue Prediction
Ahmad, I. S., Bakar, A. A., Yaakub, M. R., & Muhammad, S. H. (2020) · SN Computer Science · doi:10.1007/s42979-020-00249-1 · Cited by 34
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Quan‐Haase, A., & Young, A. L. (2014) · doi:10.1002/9781118591178.ch15 · Cited by 34
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Ovalle, A., Goldstein, O., Kachuee, M., Wu, E. S. C., Hong, C., Holloway, I. W., & Sarrafzadeh, M. (2021) · Journal of Medical Internet Research · doi:10.2196/22042 · Cited by 32
Yunpeng, S., & Khan, Y. A. (2021) · Current Psychology · doi:10.1007/s12144-021-01706-7 · Cited by 38
Real time search on the web: Queries, topics, and economic value
Jansen, B. J. J., Liu, Z., Weaver, C., Campbell, G., & Gregg, M. (2011) · Information Processing & Management · doi:10.1016/j.ipm.2011.01.007 · Cited by 32
Sentiment analysis with Twitter
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Wazery, Y. M., Mohammed, H. S., & Houssein, E. H. (2018) · International Computer Engineering Conference · doi:10.1109/icenco.2018.8636119 · Cited by 31
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Metadata
- Authors
- Asur, S., & Huberman, B. A.
- Affiliations
- Hewlett-Packard (United States) (US)
- Venue
- 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology
- Publisher
- IEEE
- Published
- 2010-08
- Pages
- 492-499
- OpenAlex
- W2015186536
- Cited by
- 2,081
- References
- 17
- Citations by year
- 2026: 12 · 2025: 36 · 2024: 52 · 2023: 71 · 2022: 90 · 2021: 129 · 2020: 168 · 2019: 156 · 2018: 165 · 2017: 196 · 2016: 184 · 2015: 243 · 2014: 205 · 2013: 173 · 2012: 111
- FWCI
- 269.17
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Funders
- National Science Foundation (0937060)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Keywords
- social media, Twitter, box office revenue, sentiment analysis, social media content
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