The DOI matches its Crossref record; 16 of the 17 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Citations
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
Flagged first, then by citations
1 of the 17 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 201–300 of 1,000
Predicting crowd behavior with big public data
Kallus, N. (2014) · International World Wide Web Conference (WWW) · doi:10.1145/2567948.2579233 · Cited by 90
Election Forecasts with Twitter - How 140 Characters Reflect the Political LandscapePreprint
Tumasjan, A., Sprenger, T. O., Sandner, P., & Welpe, I. M. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1833192 · Cited by 90
An agent-based model for emotion contagion and competition in online social media
Fan, R., Xu, K., & Zhao, J. (2017) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2017.12.086 · Cited by 89
WALKING DOWN WALL STREET WITH A TABLET: A SURVEY OF STOCK MARKET PREDICTIONS USING THE WEB
Nardo, M., Giudici, M. P., & Naltsidis, M. (2015) · Journal of Economic Surveys · doi:10.1111/joes.12102 · Cited by 91
Box office prediction based on microblog
Du, J., Xu, H., & Huang, X. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.08.065 · Cited by 88
Finding Influential Users in Social Media Using Association Rule Learning
Erlandsson, F., Bródka, P., Borg, A., & Johnson, H. (2016) · Entropy · doi:10.3390/e18050164 · Cited by 90
A survey on retail sales forecasting and prediction in fashion markets
Beheshti-Kashi, S., Karimi, H. R., Thoben, K., Lütjen, M., & Teucke, M. (2014) · Systems Science & Control Engineering · doi:10.1080/21642583.2014.999389 · Cited by 88
Forecasting sales using online review and search engine data: A method based on PCA–DSFOA–BPNN
Zhang, C., Tian, Y., & Fan, Z. (2021) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2021.07.010 · Cited by 90
Enterprise reputation threats on social media: A case of data breach framing
Syed, R. (2018) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2018.12.001 · Cited by 92
What’s Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter
Saeed, Z., Abbasi, R. A., Maqbool, O., Sadaf, A., Razzak, I., Daud, A. Y., Aljohani, N. R., & Xu, G. (2019) · Journal of Grid Computing · doi:10.1007/s10723-019-09482-2 · Cited by 86
Intelligent Disaster Response via Social Media Analysis A Survey
Nazer, T. H., Xue, G., Ji, Y., & Liu, H. (2017) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3137597.3137602 · Cited by 92
Decoupling the scholarly journal
Priem, J. R., & Hemminger, B. M. (2012) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00019 · Cited by 86
Predicting Risk Perception: New Insights from Data Science
Bhatia, S. (2019) · Management Science · doi:10.1287/mnsc.2018.3121 · Cited by 85
A novel sentiment analysis of social networks using supervised learning
Anjaria, M., & Guddeti, R. M. R. (2014) · Social Network Analysis and Mining · doi:10.1007/s13278-014-0181-9 · Cited by 83
Policing cyber-neighbourhoods: tension monitoring and social media networks
Williams, M. L., Edwards, A., Housley, W., Burnap, P., RANA, O. F., Avis, N. J., Morgan, J., & Sloan, L. (2013) · Policing & Society · doi:10.1080/10439463.2013.780225 · Cited by 83
Using friends as sensors to detect planetary-scale contagious outbreaks
Cebrián, M. (2012) · doi:10.1145/2390034.2390041 · Cited by 83
Du, E., Cai, X., Sun, Z., & Minsker, B. S. (2017) · Water Resources Research · doi:10.1002/2017wr021192 · Cited by 86
Identifying customer knowledge on social media through data analytics
He, W., Zhang, W., Tian, X., Tao, R., & Akula, V. (2018) · Journal of Enterprise Information Management · doi:10.1108/jeim-02-2018-0031 · Cited by 85
Social media prediction: a literature review
Rousidis, D., Koukaras, P., & Tjortjis, C. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08291-9 · Cited by 82
Polignano, M., Narducci, F., de Gemmis, M., & Semeraro, G. M. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114382 · Cited by 81
Younis, E. M. G. (2015) · Cited by 80
Investor sentiment in the theoretical field of behavioural finance
Cabarcos, Á. L., Pérez-Pico, A. M., Rodríguez, P. V., & López-Pérez, M. L. (2019) · Economic Research-Ekonomska Istraživanja · doi:10.1080/1331677x.2018.1559748 · Cited by 107
Performance analysis of in-network caching for content-centric networking
Kim, Y., & Yeom, I. (2013) · Computer Networks · doi:10.1016/j.comnet.2012.11.026 · Cited by 79
Predicting movie Box-office revenues by exploiting large-scale social media content
Liu, T., Ding, X., Chen, Y., Chen, H., & Guo, M. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2270-1 · Cited by 78
Emotional Divergence Influences Information Spreading in Twitter
Pfitzner, R., Garas, A., & Schweitzer, F. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v6i1.14294 · Cited by 76
Antretter, T., Blohm, I., Grichnik, D., & Wincent, J. (2019) · Journal of Business Venturing Insights · doi:10.1016/j.jbvi.2018.e00109 · Cited by 75
TWITTER IMPROVES SEASONAL INFLUENZA PREDICTION
Achrekar, H., Gandhe, A., Lazarus, R. M., Yu, S.-H., & Liu, B. (2012) · International Conference on Health Informatics · doi:10.5220/0003780600610070 · Cited by 77
Motives, Modes of Participation, and Loyalty Intentions of Facebook Tourism Brand Page Consumers
Ben-Shaul, M., & Reichel, A. (2017) · Journal of Travel Research · doi:10.1177/0047287517704087 · Cited by 79
Successes and challenges of Arabic sentiment analysis research: a literature review
El-Masri, M., Altrabsheh, N., & Mansour, H. (2017) · Social Network Analysis and Mining · doi:10.1007/s13278-017-0474-x · Cited by 77
Extracting Emotions from Texts in E-Learning Environments
Rodrı́guez, P., Ortigosa, Á., & Carro, R. M. (2012) · ... International Conference on Complex, Intelligent and Software Intensive Systems · doi:10.1109/cisis.2012.192 · Cited by 76
From classification to quantification in tweet sentiment analysis
Gao, W., & Sebastiani, F. (2016) · Social Network Analysis and Mining · doi:10.1007/s13278-016-0327-z · Cited by 75
Quantifying Collective Attention from Tweet Stream
Sasahara, K., Hirata, Y., Toyoda, M., Kitsuregawa, M., & Aihara, K. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061823 · Cited by 74
Early indicators of scientific impact: Predicting citations with altmetrics
Akella, A. P., Alhoori, H., Kondamudi, P. R., Freeman, C., & Zhou, H. (2021) · Journal of Informetrics · doi:10.1016/j.joi.2020.101128 · Cited by 74
Twitter as a predictive system: A systematic literature review
Caño-Marín, E., Mora‐Cantallops, M., & Sánchez‐Alonso, S. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.113561 · Cited by 90
Drivers and challenges of precision agriculture: a social media perspective
Ofori, M. Q., & El-Gayar, O. (2020) · Precision Agriculture · doi:10.1007/s11119-020-09760-0 · Cited by 73
Elsayed, W. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e06327 · Cited by 73
Predicting Asset Value through Twitter Buzz
Zhang, X., Fuehres, H., & Gloor, P. A. (2012) · Advances in intelligent and soft computing · doi:10.1007/978-3-642-25321-8_3 · Cited by 73
"Manipulation and abuse on social media" by Emilio Ferrara with Ching-man Au Yeung as coordinator
Ferrara, E. (2015) · ACM SIGWEB Newsletter · doi:10.1145/2749279.2749283 · Cited by 73
Movie Revenue Prediction Based on Purchase Intention Mining Using YouTube Trailer Reviews
Ahmad, I. S., Bakar, A. A., & Yaakub, M. R. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102278 · Cited by 72
Social media as a new playing field for the governance of agro-food sustainability
Stevens, T. M., Aarts, N., Termeer, C., & Dewulf, A. R. P. J. (2016) · Current Opinion in Environmental Sustainability · doi:10.1016/j.cosust.2015.11.010 · Cited by 74
T-Hoarder: A framework to process Twitter data streams
Congosto, M., Val, P. B., & Fernández, L. S. (2017) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2017.01.029 · Cited by 77
Duan, H. K., Vasarhelyi, M. A., Codesso, M. M., & Alzamil, Z. S. (2022) · International Journal of Accounting Information Systems · doi:10.1016/j.accinf.2022.100600 · Cited by 70
Advertising to Early Trend Propagators: Evidence from Twitter
Lambrecht, A., Tucker, C. E., & Wiertz, C. (2014) · Marketing Science · doi:10.1287/mksc.2017.1062 · Cited by 87
Leveraging candidate popularity on Twitter to predict election outcome
Gaurav, M., Srivastava, A. K., Kumar, A., & Miller, S. (2013) · Social Network Mining and Analysis · doi:10.1145/2501025.2501038 · Cited by 69
An exploratory study of Twitter messages about software applications
Guzmán, E., Alkadhi, R., & Seyff, N. (2017) · Requirements Engineering · doi:10.1007/s00766-017-0274-x · Cited by 71
Electronic word-of-mouth, box office revenue and social media
Baek, H., Oh, S., Yang, H., & Ahn, J. (2017) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2017.02.001 · Cited by 68
Social Media Big Data Analytics for Demand Forecasting
Iftikhar, R., & Khan, M. S. (2019) · Journal of Global Information Management · doi:10.4018/jgim.2020010106 · Cited by 67
Taxonomy and Evaluation for Microblog Popularity Prediction
Gao, X., Cao, Z., Li, S., Yao, B., Chen, G., & Tang, S. (2019) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3301303 · Cited by 67
Liu, K., Zhou, J., & Dong, D. (2021) · Journal of Behavioral and Experimental Finance · doi:10.1016/j.jbef.2021.100507 · Cited by 68
Movie Popularity and Target Audience Prediction Using the Content-Based Recommender System
Sahu, S., Kumar, R., Pathan, M. M., Shafi, J., Kumar, Y., & Ijaz, M. F. (2022) · IEEE Access · doi:10.1109/access.2022.3168161 · Cited by 66
Police message diffusion on Twitter: analysing the reach of social media communications
van de Velde, B., Meijer, A., & Homburg, V. (2015) · Behavior and Information Technology · doi:10.1080/0144929x.2014.942754 · Cited by 65
Influential user weighted sentiment analysis on topic based microblogging community
Eliaçik, A. B., & Erdoğan, N. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.10.006 · Cited by 66
Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ1 Regularization
Hernandez-Suarez, A., Sánchez-Pérez, G., Toscano-Medina, K., Martinez-Hernandez, V., Perez-Meana, H. M., Olivares-Mercado, J., & Sánchez, V. (2018) · Sensors · doi:10.3390/s18051380 · Cited by 66
A Tutorial on Interactive Sensing in Social Networks
Krishnamurthy, V., & Poor, H. V. (2014) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2014.2307452 · Cited by 64
Using Twitter to learn about the autism community
Beykikhoshk, A., Arandjelović, O., Phung, D., Venkatesh, S., & Caelli, T. (2015) · Social Network Analysis and Mining · doi:10.1007/s13278-015-0261-5 · Cited by 68
Predicting iPhone Sales from iPhone Tweets
Lassen, N. B., Madsen, R., & Vatrapu, R. (2014) · IEEE International Enterprise Distributed Object Computing Conference (EDOC) · doi:10.1109/edoc.2014.20 · Cited by 67
Facebook likes and public opinion: Predicting the 2015 Finnish parliamentary elections
Vepsäläinen, T., Li, H., & Suomi, R. (2017) · Government Information Quarterly · doi:10.1016/j.giq.2017.05.004 · Cited by 65
Manipulation and abuse on social mediaPreprint
Ferrara, E. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1503.03752 · Cited by 65
Jurado, F., & Rodrı́guez, P. (2015) · Journal of Systems and Software · doi:10.1016/j.jss.2015.02.055 · Cited by 65
What’s in Twitter, I know what parties are popular and who you are supporting now!
Boutet, A., Kim, H., & Yoneki, E. (2013) · Social Network Analysis and Mining · doi:10.1007/s13278-013-0120-1 · Cited by 64
Prediction models for network-linked data
Li, T., Levina, E., & Zhu, J. (2019) · The Annals of Applied Statistics · doi:10.1214/18-aoas1205 · Cited by 73
Tuarob, S., & Tucker, C. S. (2013) · doi:10.1115/detc2013-12661 · Cited by 63
Assessing the reTweet proneness of tweets: predictive models for retweeting
Nesi, P., Pantaleo, G., Paoli, I., & Zaza, I. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5865-0 · Cited by 62
Mining Twitter data for influenza detection and surveillance
Byrd, K., Mansurov, A., & Baysal, O. (2016) · IEEE/ACM International Workshop on Software Engineering for Healthcare (SEH) · doi:10.1145/2897683.2897693 · Cited by 63
Measuring economic activity in China with mobile big data
Dong, L., Chen, S., Cheng, Y., Wu, Z., Li, C., & Wu, H. (2017) · EPJ Data Science · doi:10.1140/epjds/s13688-017-0125-5 · Cited by 67
Social Monitoring for Public Health
Paul, M. J., & Dredze, M. H. (2017) · Synthesis lectures on information concepts, retrieval, and services · doi:10.1007/978-3-031-02311-8 · Cited by 66
The role of social media data in operations and production management
Chan, H. K., Lacka, E., Yee, R. W. Y., & Lim, M. K. (2015) · International Journal of Production Research · doi:10.1080/00207543.2015.1053998 · Cited by 64
Sentiment Analysis of Twitter Data within Big Data Distributed Environment for Stock Prediction
Skuza, M., & Romanowski, A. (2015) · Annals of Computer Science and Information Systems · doi:10.15439/2015f230 · Cited by 62
I Like, I Cite? Do Facebook Likes Predict the Impact of Scientific Work?
Ringelhan, S., Wollersheim, J., & Welpe, I. M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0134389 · Cited by 69
Social Media Analytics and Business Value: A Theoretical Framework and Case Study
Bekmamedova, N., & Shanks, G. (2014) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2014.464 · Cited by 62
A classification of user-generated content into consumer decision journey stages
Vázquez, S. R., Muñoz-García, Ó., Campanella, I., Poch, M., Fisas, B., Bel, N., & Andreu, G. (2014) · Neural Networks · doi:10.1016/j.neunet.2014.05.026 · Cited by 62
A new temporal and social PMF-based method to predict users' interests in micro-blogging
Bao, H., Li, Q., Liao, S. S., Song, S., & Gao, H. (2013) · Decision Support Systems · doi:10.1016/j.dss.2013.02.007 · Cited by 62
Sentiment analysis on Twitter data with semi-supervised Doc2Vec
Bi̇lgi̇n, M., & Senturk, I. F. (2017) · doi:10.1109/ubmk.2017.8093492 · Cited by 62
Forecasting Social Unrest Using Activity Cascades
Cadena, J., Korkmaz, G., Kuhlman, C. J., Marathe, A., Ramakrishnan, N., & Vullikanti, A. K. S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0128879 · Cited by 62
Divided They Tweet: The Network Structure of Political Microbloggers and Discussion Topics
Feller, A., Kuhnert, M., Sprenger, T. O., & Welpe, I. M. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14150 · Cited by 61
Detecting Campaign Promoters on Twitter Using Markov Random Fields
Li, H., Mukherjee, A., Liu, B., Kornfield, R., & Emery, S. (2014) · Proceedings · doi:10.1109/icdm.2014.59 · Cited by 61
Optimal Content Placement for a Large-Scale VoD System
Applegate, D., Archer, A. F., Gopalakrishnan, V., Lee, S., & Ramakrishnan, K. K. (2015) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2015.2461599 · Cited by 60
González‐Bailón, S., Banchs, R. E., & Kaltenbrunner, A. (2012) · Human Communication Research · doi:10.1111/j.1468-2958.2011.01423.x · Cited by 60
Detecting Non‐personal and Spam Users on Geo‐tagged Twitter Network
Guo, D., & Chen, C. (2014) · Transactions in GIS · doi:10.1111/tgis.12101 · Cited by 60
Score a tweet and post a goal: Social media recipes for sports stars
Korzyński, P., & Paniagua, J. (2015) · Business Horizons · doi:10.1016/j.bushor.2015.11.002 · Cited by 60
Predicting movie success with machine learning techniques: ways to improve accuracy
Lee, K., Park, J., Kim, I., & Choi, Y. T. (2016) · Information Systems Frontiers · doi:10.1007/s10796-016-9689-z · Cited by 60
Singh, A., & Tucker, C. S. (2017) · Decision Support Systems · doi:10.1016/j.dss.2017.03.007 · Cited by 58
COSMOS: Towards an integrated and scalable service for analysing social media on demand
Burnap, P., RANA, O. F., Williams, M. L., Housley, W., Edwards, A., Morgan, J., Sloan, L., & Conejero, J. (2014) · International Journal of Parallel Emergent and Distributed Systems · doi:10.1080/17445760.2014.902057 · Cited by 59
End Users' Perception of Hybrid Mobile Apps in the Google Play Store
Malavolta, I., Ruberto, S., Soru, T., & Terragni, V. (2015) · IEEE International Conference on Mobile Services · doi:10.1109/mobserv.2015.14 · Cited by 59
Twitter Sentiment Analysis: How to Hedge Your Bets in the Stock Markets
Rao, T., & Srivastava, S. (2014) · Lecture notes in social networks · doi:10.1007/978-3-319-05912-9_11 · Cited by 59
Cvijikj, I. P., & Michahelles, F. (2011) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc.2011.150 · Cited by 59
Forecasting Popularity of Videos Using Social Media
Xu, J., van der Schaar, M., Liu, J., & Li, H. (2014) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2014.2370942 · Cited by 59
The relationship between twitter and stock prices. Evidence from the US technology industry
Teti, E., Dallocchio, M., & Aniasi, A. (2019) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2019.119747 · Cited by 59
#iamhappybecause: Gross National Happiness through Twitter analysis and big data
Durahim, A. O., & Coşkun, M. (2015) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2015.06.035 · Cited by 59
Early Stage Influenza Detection from TwitterPreprint
Li, J., & Cardie, C. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1309.7340 · Cited by 59
Prediction of the 2017 French election based on Twitter data analysis
Wang, L., & Gan, J. Q. (2017) · Computer Science and Electronic Engineering Conference · doi:10.1109/ceec.2017.8101605 · Cited by 58
Unifying Algorithmic and Theoretical Perspectives: Emotions in Online Reviews and Sales
Yu, Y., Yang, Y., Huang, J., & Tan, Y. (2023) · MIS Quarterly · doi:10.25300/misq/2022/16600 · Cited by 55
Foresight 2.0 - Definition, overview & evaluation
Schatzmann, J., Schäfer, R., & Eichelbaum, F. (2013) · European Journal of Futures Research · doi:10.1007/s40309-013-0015-4 · Cited by 58
Using Twitter data to predict the performance of Bollywood movies
Gaikar, D. D., Marakarkandy, B., & Dasgupta, C. (2015) · Industrial Management & Data Systems · doi:10.1108/imds-04-2015-0145 · Cited by 58
SES: Sentiment Elicitation System for Social Media Data
Zhang, K., Cheng, Y., Xie, Y., Honbo, D., Agrawal, A., Palsetia, D., Lee, K., Liao, W., & Choudhary, A. (2011) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2011.153 · Cited by 58
Agostino, D., & Sidorova, Y. (2016) · Measuring Business Excellence · doi:10.1108/mbe-05-2015-0030 · Cited by 58
Inventory Management in the Era of Big Data
Bertsimas, D., Kallus, N., & Hussain, A. (2016) · Production and Operations Management · doi:10.1111/poms.2_12637 · Cited by 58
Jungherr, A. M., & Jürgens, P. (2013) · Internet Research · doi:10.1108/intr-06-2012-0115 · Cited by 57
Social media research: A review
Wu, J., Sun, H., & Tan, Y. (2013) · Journal of Systems Science and Systems Engineering · doi:10.1007/s11518-013-5225-6 · Cited by 60
Keller‐McNulty, S., Korkmaz, G., Orr, M., Schroeder, A. D., & Shipp, S. S. (2017) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-060116-054114 · Cited by 68
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
