Predicting the Future with Social Media

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

Why we twitter

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

Large-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

Predicting Movie Sales from Blogger Sentiment

Mishne, G., & Glance, N. (2006) · Cited by 354

Early Predictions of Movie Success: The Who, What, and When of Profitability

Lash, M. T., & Zhao, K. (2016) · Journal of Management Information Systems · doi:10.1080/07421222.2016.1243969 · Cited by 183

INVESTIGATING PREDICTIVE POWER OF STOCK MICRO BLOG SENTIMENT IN FORECASTING FUTURE STOCK PRICE DIRECTIONAL MOVEMENT

Oh, C., & Sheng, O. R. L. (2011) · Journal of the Association for Information Systems · Cited by 182

Everything as a Service: Powering the New Information Economy

Banerjee, P., Friedrich, R., Bash, C., Goldsack, P., Huberman, B., Manley, J., Patel, C., Ranganathan, P., & Veitch, A. (2011) · Computer · doi:10.1109/mc.2011.67 · Cited by 208

Social media metrics and analytics in marketing – S3M: A mapping literature review

Misirlis, N., & Vlachopoulou, M. (2017) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2017.10.005 · Cited by 181

TM-LDA

Wang, Y., Agichtein, E., & Michele Benzi, A. (2012) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339552 · Cited by 180

"I Wanted to Predict Elections with Twitter and all I got was this Lousy Paper" -- A Balanced Survey on Election Prediction using Twitter Data

Gayo-Avello, D. (2012) · arXiv · doi:10.48550/arxiv.1204.6441 · Cited by 179

Improved lexicon-based sentiment analysis for social media analytics

Jurek, A., Mulvenna, M. D., & Bi, Y. (2015) · Security Informatics · doi:10.1186/s13388-015-0024-x · Cited by 177

Twitter Usage in IndonesiaPreprint

Carley, K. M., Malik, M. M., Landwehr, P. M., Pfeffer, J., & Kowalchuck, M. J. (2015) · SSRN Electronic Journal · doi:10.1016/j.ssci.2016.04.002 · Cited by 236

A peek into the future

Ahmed, M., Spagna, S., Huici, F., & Niccolini, S. (2013) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2433396.2433473 · Cited by 173

Detecting tension in online communities with computational Twitter analysis

Burnap, P., RANA, O. F., Avis, N. J., Williams, M. L., Housley, W., Edwards, A., Morgan, J., & Sloan, L. (2013) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2013.04.013 · Cited by 170

Use of Social Media and its Impact on Academic Performance of Tertiary Institution Students: A Study of Students of Koforidua Polytechnic, Ghana

Owusu-Acheaw, M., & Larson, A. G. (2015) · Journals & Books Hosting (International Knowledge Sharing Platform) · Cited by 169

Forecasting with twitter data

Arias, M., Arratia, A., & Xuriguera, R. (2013) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2542182.2542190 · Cited by 169

Measuring patient-perceived quality of care in US hospitals using Twitter

Hawkins, J. B., Brownstein, J. S., Tuli, G., Runels, T., Broecker, K., Nsoesie, E. O., McIver, D. J., Rozenblum, R., Wright, A. T., Bourgeois, F. T., et al. (2015) · BMJ Quality & Safety · doi:10.1136/bmjqs-2015-004309 · Cited by 168

Risk perception and communication in vaccination decisions: A fuzzy-trace theory approach

Reyna, V. F. (2011) · Vaccine · doi:10.1016/j.vaccine.2011.11.070 · Cited by 167

Research trends in social media addiction and problematic social media use: A bibliometric analysis

Pellegrino, A., Stasi, A., & Bhatiasevi, V. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.1017506 · Cited by 212

Linking social media to customer relationship management (CRM): a qualitative study on SMEs

Guha, S., Harrigan, P., & Soutar, G. N. (2017) · Journal of Small Business & Entrepreneurship · doi:10.1080/08276331.2017.1399628 · Cited by 170

To Link or Not to Link? A Study on End-to-End Tweet Entity Linking

Guo, S. D., Chang, M., & Kıcıman, E. (2013) · Cited by 153

Modeling and Predicting Retweeting Dynamics on Microblogging Platforms

Gao, S., Ma, J., & Chen, Z. (2015) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2684822.2685303 · Cited by 153

Arabic tweets sentiment analysis – a hybrid scheme

Aldayel, H. K., & Azmi, A. M. (2015) · Journal of Information Science · doi:10.1177/0165551515610513 · Cited by 153

Report of the AAPOR task force on non-probability sampling

Baker, R., Brick, J. M., Bates, N., Battaglia, M., Couper, M. P., Dever, J. A., Gile, K. J., & Tourangeau, R. (2013) · Cited by 151

Not all digital word of mouth is created equal: Understanding the respective impact of consumer reviews and microblogs on new product success

Marchand, A., Hennig‐Thurau, T., & Wiertz, C. (2016) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2016.09.003 · Cited by 162

Marketplace Sentiments

Gopaldas, A. (2014) · Journal of Consumer Research · doi:10.1086/678034 · Cited by 150

UTiLearn: A Personalised Ubiquitous Teaching and Learning System for Smart Societies

Mehmood, R., Alam, F., Albogami, N., Katib, I. A., Albeshri, A. A., & Altowaijri, S. M. (2017) · IEEE Access · doi:10.1109/access.2017.2668840 · Cited by 151

Tweets and Votes: A Study of the 2011 Singapore General Election

Škorić, M. M., Poor, N. D., Achananuparp, P., Lim, E., & Jiang, J. (2012) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2012.607 · Cited by 148

Exploring the effect of user engagement in online brand communities: Evidence from Twitter

Ibrahim, N. F., Wang, X., & Bourne, H. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.03.005 · Cited by 148

Using User- and Marketer-Generated Content for Box Office Revenue Prediction: Differences Between Microblogging and Third-Party Platforms

Song, T., Huang, J., Tan, Y., & Yu, Y. (2019) · Information Systems Research · doi:10.1287/isre.2018.0797 · Cited by 148

Don't turn social media into another 'Literary Digest' poll

Gayo-Avello, D. (2011) · Communications of the ACM · doi:10.1145/2001269.2001297 · Cited by 147

Twitter Sentiment Analysis: Lexicon Method, Machine Learning Method and Their CombinationPreprint

Kolchyna, O., Souza, T. T. P., Treleaven, P. C., & Aste, T. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.00955 · Cited by 216

A Demographic Analysis of Online Sentiment during Hurricane Irene

Mandel, B. A., Culotta, A., Boulahanis, J. G., Stark, D., Lewis, B. L., & Rodrigue, J. (2012) · Cited by 146

Predicting IMDB Movie Ratings Using Social Media

Oghina, A., Breuss, M., Tsagkias, M., & de Rijke, M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28997-2_51 · Cited by 143

Exploring Limits to Prediction in Complex Social Systems

Martin, T., Hofman, J. M., Sharma, A. K., Anderson, A., & Watts, D. J. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872427.2883001 · Cited by 147

Crime Sensing with Big Data: The Affordances and Limitations of using Open Source Communications to Estimate Crime Patterns

Williams, M. L., Burnap, P., & Sloan, L. (2016) · The British Journal of Criminology · doi:10.1093/bjc/azw031 · Cited by 142

Exploration of social media for sentiment analysis using deep learning

Chen, L.-C., Lee, C.-M., & Chen, M. (2019) · Soft Computing · doi:10.1007/s00500-019-04402-8 · Cited by 147

Wisdom of Crowds: Cross‐Sectional Variation in the Informativeness of Third‐Party‐Generated Product Information on Twitter

Tang, V. W. (2017) · Journal of Accounting Research · doi:10.1111/1475-679x.12183 · Cited by 138

Crime prediction using Twitter sentiment and weather

Chen, X., Cho, Y., & Jang, S.-H. (2015) · Systems and Information Engineering Design Symposium · doi:10.1109/sieds.2015.7117012 · Cited by 128

When Can Social Media Lead Financial Markets?

Zheludev, I., Smith, R. E., & Aste, T. (2014) · Scientific Reports · doi:10.1038/srep04213 · Cited by 144

Towards Cross-Domain Learning for Social Video Popularity Prediction

Roy, S. D., Mei, T., Zeng, W., & Li, S. (2013) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2013.2265079 · Cited by 136

Predictive Sentiment Analysis of Tweets: A Stock Market Application

Smailović, J., Grćar, M., Lavrač, N., & Žnidaršič, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39146-0_8 · Cited by 139

How does Google search affect trader positions and crude oil prices?

Li, X., Ma, J., Wang, S., & Zhang, X. (2015) · Economic Modelling · doi:10.1016/j.econmod.2015.04.005 · Cited by 133

The power of the “like” button: The impact of social media on box office

Ding, C., Cheng, H. K., Duan, Y., & Jin, Y. J. (2016) · Decision Support Systems · doi:10.1016/j.dss.2016.11.002 · Cited by 133

Social media analytics and business intelligence research: A systematic review

Choi, J., Yoon, J., Chung, J., Coh, B., & Lee, J.-M. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102279 · Cited by 144

Predicting and Interpolating State‐Level Polls Using Twitter Textual Data

Beauchamp, N. (2016) · American Journal of Political Science · doi:10.1111/ajps.12274 · Cited by 140

Predicting Disease Transmission from Geo-Tagged Micro-Blog Data

Sadilek, A., Kautz, H., & Silenzio, V. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v26i1.8103 · Cited by 131

Vocal Minority Versus Silent Majority: Discovering the Opionions of the Long Tail

Mustafaraj, E., Finn, S., Whitlock, C., & Metaxas, P. (2011) · International Conference on Social Computing · doi:10.1109/passat/socialcom.2011.188 · Cited by 131

Twitter as a tool for forecasting stock market movements: A short-window event study

Nisar, T. M., & Yeung, M. W. (2018) · The Journal of Finance and Data Science · doi:10.1016/j.jfds.2017.11.002 · Cited by 131

Using Social Media to Detect Outdoor Air Pollution and Monitor Air Quality Index (AQI): A Geo-Targeted Spatiotemporal Analysis Framework with Sina Weibo (Chinese Twitter)

Jiang, W., Wang, Y., Tsou, M., & Fu, X. (2015) · PLoS ONE · doi:10.1371/journal.pone.0141185 · Cited by 130

Computational social science: Making the links

Giles, J. (2012) · Nature · doi:10.1038/488448a · Cited by 130

Influence factor based opinion mining of Twitter data using supervised learning

Anjaria, M., & Guddeti, R. M. R. (2014) · doi:10.1109/comsnets.2014.6734907 · Cited by 128

Making sense of social media streams through semantics: A survey

Bontcheva, K., & Rout, D. (2014) · Semantic Web · doi:10.3233/sw-130110 · Cited by 128

Combating Rumor Spread on Social Media: The Effectiveness of Refutation and Warning

Ozturk, P., Li, H., & Sakamoto, Y. (2015) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2015.288 · Cited by 126

Mining social media with social theories

Tang, J., Chang, Y., & Liu, H. (2014) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2641190.2641195 · Cited by 126

Clickstream Data and Inventory Management: Model and Empirical Analysis

Huang, T., & Mieghem, J. A. V. (2011) · Production and Operations Management · doi:10.1111/poms.12046 · Cited by 125

What's in a Name? Understanding the Interplay between Titles, Content, and Communities in Social Media

Lakkaraju, H., McAuley, J., & Leskovec, J. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14408 · Cited by 125

Digital social research, social media and the sociological imagination: surrogacy, augmentation and re-orientation

Edwards, A., Housley, W., Williams, M. L., Sloan, L., & Williams, M. (2013) · International Journal of Social Research Methodology · doi:10.1080/13645579.2013.774185 · Cited by 120

Predicting National Suicide Numbers with Social Media Data

Won, H., Myung, W., Song, G.-Y., Lee, W.-H., Kim, J., Carroll, B. J., & Kim, D. K. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061809 · Cited by 120

Pre-production forecasting of movie revenues with a dynamic artificial neural network

Ghiassi, M., Lio, D., & Moon, B. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.11.022 · Cited by 127

Public Sentiment Analysis in Twitter Data for Prediction of a Company's Stock Price Movements

Li, B., Chan, K. C. C., & Ou, C. X. (2014) · IEEE International Conference on e-Business Engineering (ICEBE) · doi:10.1109/icebe.2014.47 · Cited by 117

Digital Trace Data in the Study of Public Opinion

Jungherr, A. M., Schoen, H., Posegga, O., & Jürgens, P. (2016) · Social Science Computer Review · doi:10.1177/0894439316631043 · Cited by 131

Metamodel for integration of Internet of Things, Social Networks, the Cloud and Industry 4.0

Molano, J. I. R., Lovelle, J. M. C., Montenegro, C., Granados, J. J. R., & Crespo, R. A. G. (2017) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-017-0469-5 · Cited by 122

Understanding users’ continuous content contribution behaviours on microblogs: an integrated perspective of uses and gratification theory and social influence theory

Liu, X., Min, Q., & Han, S. (2019) · Behaviour and Information Technology · doi:10.1080/0144929x.2019.1603326 · Cited by 144

Discovering public sentiment in social media for predicting stock movement of publicly listed companies

Li, B., Chan, K. C. C., Ou, C. X., & Ruifeng, S. (2017) · Information Systems · doi:10.1016/j.is.2016.10.001 · Cited by 124

Social media metrics and sentiment analysis to evaluate the effectiveness of social media posts

Poecze, F., Ebster, C., & Strauß, C. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.04.117 · Cited by 115

The Value of Weather Information for E‐Commerce Operations

Steinker, S., Hoberg, K., & Thonemann, U. W. (2017) · Production and Operations Management · doi:10.1111/poms.12721 · Cited by 114

Testing an extended model of consumer behavior in the context of social media-based brand communities

Habibi, M. R., Laroche, M., & Richard, M. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.03.079 · Cited by 124

Tweeting the Meeting: An In-Depth Analysis of Twitter Activity at Kidney Week 2011

Desai, T., Shariff, A. I., Shariff, A., Кац, М. Д., Fang, X., Christiano, C., & Ferris, M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040253 · Cited by 111

A keyword extraction method from twitter messages represented as graphs

Abilhoa, W. D., & de Castro, L. N. (2014) · Applied Mathematics and Computation · doi:10.1016/j.amc.2014.04.090 · Cited by 111

Investigating the antecedents of e-commerce satisfaction in social commerce context

Attar, R. W., Shanmugam, M., & Hajli, N. (2020) · British Food Journal · doi:10.1108/bfj-08-2020-0755 · Cited by 135

The dynamics of information-driven coordination phenomena: A transfer entropy analysis

Borge‐Holthoefer, J., Perra, N., Gonçalves, B., González-Bailón, S., Arenas, À., Moreno, Y., & Vespignani, A. (2016) · Science Advances · doi:10.1126/sciadv.1501158 · Cited by 110

S-MART: Novel Tree-based Structured Learning Algorithms Applied to Tweet Entity Linking

Yang, Y., & Chang, M.-W. (2015) · doi:10.3115/v1/p15-1049 · Cited by 110

Predicting the popularity of online articles based on user comments

Tatar, A., Leguay, J., Antoniadis, P., Limbourg, A., de Amorim, M. D., & Serge Fdida, B. (2011) · International Conference on Web Intelligence, Mining and Semantics · doi:10.1145/1988688.1988766 · Cited by 109

Quantifying Product Favorability and Extracting Notable Product Features Using Large Scale Social Media Data

Tuarob, S., & Tucker, C. S. (2015) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4029562 · Cited by 109

The Effect of Social Media on Personal Branding Efforts of Top Level Executives

Karaduman, İ. (2013) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2013.10.515 · Cited by 120

Smoothing techniques for adaptive online language models

Lin, J., Snow, R., & Morgan, W. (2011) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020476 · Cited by 109

The Role of Pre-processing in Twitter Sentiment Analysis

Bao, Y., Quan, C., Wang, L., & Ren, F. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-09339-0_62 · Cited by 107

Box office forecasting using machine learning algorithms based on SNS data

Kim, T., Hong, J.-S., & Kang, P. (2015) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2014.05.006 · Cited by 107

Big Data Analytics for Emergency Communication Networks: A Survey

Wang, J., Wu, Y., Yen, N. Y., Guo, S., & Cheng, Z. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2540004 · Cited by 108

How does market concern derived from the Internet affect oil prices?

Guo, J., & Ji, Q. (2013) · Applied Energy · doi:10.1016/j.apenergy.2013.03.027 · Cited by 106

Blog mining-review and extensions: “From each according to his opinion”

O’Leary, D. E. (2011) · Decision Support Systems · doi:10.1016/j.dss.2011.01.016 · Cited by 106

The Evaluative Lexicon 2.0: The measurement of emotionality, extremity, and valence in language

Rocklage, M. D., Rucker, D. D., & Nordgren, L. F. (2017) · Behavior Research Methods · doi:10.3758/s13428-017-0975-6 · Cited by 106

Agenda Trending: Reciprocity and the Predictive Capacity of Social Networking Sites in Intermedia Agenda Setting across Topics over Time

Groshek, J., & Groshek, M. C. (2013) · Media and Communication · doi:10.17645/mac.v1i1.71 · Cited by 104

A Survey of Prediction Using Social MediaPreprint

Yu, S., & Kak, S. C. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1203.1647 · Cited by 104

Predicting collective sentiment dynamics from time-series social media

Nguyen, L. T., Wu, P., Chan, W., Peng, W., & Zhang, Y. (2012) · International Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM) · doi:10.1145/2346676.2346682 · Cited by 103

Social Set Analysis: A Set Theoretical Approach to Big Data Analytics

Vatrapu, R., Mukkamala, R. R., Hussain, A., & Flesch, B. (2016) · IEEE Access · doi:10.1109/access.2016.2559584 · Cited by 105

Do Cascades Recur?

Cheng, J., Adamic, L. A., Kleinberg, J. M., & Leskovec, J. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872427.2882993 · Cited by 102

Predicting Responses to Microblog Posts

Artzi, Y., Pantel, P., & Gamon, M. (2012) · Cited by 100

Predictability of Popularity: Gaps between Prediction and Understanding

Shulman, B., Sharma, A., & Cosley, D. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v10i1.14748 · Cited by 100

Big and broad social data and the sociological imagination: A collaborative response

Housley, W., Procter, R., Edwards, A., Burnap, P., Williams, M. L., Sloan, L., RANA, O. F., Morgan, J., Alex Voss, L., & Greenhill, A. (2014) · Big Data & Society · doi:10.1177/2053951714545135 · Cited by 98

The Double-Edged Sword of Big Data in Organizational and Management Research

Wenzel, R., & Quaquebeke, N. V. (2017) · Organizational Research Methods · doi:10.1177/1094428117718627 · Cited by 107

Using Friends as Sensors to Detect Global-Scale Contagious Outbreaks

García–Herranz, M., Moro, E., Cebrián, M., Christakis, N. A., & Fowler, J. H. (2014) · PLoS ONE · doi:10.1371/journal.pone.0092413 · Cited by 97

Prerelease Buzz Evolution Patterns and New Product Performance

Xiong, G., & Bharadwaj, S. G. (2014) · Marketing Science · doi:10.1287/mksc.2013.0828 · Cited by 95

Detecting discussion communities on vaccination in twitter

Bello-Orgaz, G., Hernandez-Castro, J. C., & Camacho, D. (2016) · Future Generation Computer Systems · doi:10.1016/j.future.2016.06.032 · Cited by 92

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries

Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763

Stock Prediction Using Event-Based Sentiment Analysis

Makrehchi, M., Shah, S., & Liao, W. (2013) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat.2013.48 · Cited by 94

Mapping collective behavior in the big-data era

Bentley, R. A., O’Brien, M. J., & Brock, W. A. (2014) · Behavioral and Brain Sciences · doi:10.1017/s0140525x13000289 · Cited by 95

Visual Sentiment Analysis for Social Images Using Transfer Learning Approach

Islam, J., & Zhang, Y. (2016) · doi:10.1109/bdcloud-socialcom-sustaincom.2016.29 · Cited by 94

Machine Learning Refined

Watt, J. M., Borhani, R., & Katsaggelos, A. K. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108690935 · Cited by 94

Exploring global food system shocks, scenarios and outcomes

Hamilton, H., Henry, R., Rounsevell, M., Moran, D., Cossar, F., Allen, K., Boden, L., & Alexander, P. M. (2020) · Futures · doi:10.1016/j.futures.2020.102601 · Cited by 91

Machine Learning Refined

Watt, J. M., Borhani, R., & Katsaggelos, A. K. (2016) · Cambridge University Press eBooks · doi:10.1017/cbo9781316402276 · Cited by 96

Social sensors and pervasive services: Approaches and perspectives

Alberto Rosi, A., Mamei, M., Zambonelli, F., Dobson, S., Stevenson, G., & Ye, J. (2011) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2011.5766946 · Cited by 90

Identifying Correlated Bots in Twitter

Chavoshi, N., Hamooni, H., & Mueen, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-47874-6_2 · Cited by 90

Approved for External Publication © Copyright 2010 Hewlett-Packard Development Company

Asur, S., & Huberman, B. A. (2014)

Scientific Journal Impact Factor: 3.449 (ISRA), Impact Factor: 2.114

Patel, P., Patel, R., & Agrawal, E. (2016)

Social Media Mining

Choudhery, D., & Leung, C. K.-S. (2017) · International Database Engineering and Applications Symposium (IDEAS) · doi:10.1145/3105831.3105854 · Cited by 45

The power of twitter on predicting box office revenues

Jeon, J., & McSharry, P. (2012) · Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) · Cited by 2

Predictive Model for Box-office Success of Movies Using Social media

Thite, P., Shah, N., & Marakarkandy, B. (2014)