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

Properties of Vector Embeddings in Social Networks

Rizi, F. S., & Granitzer, M. (2017) · Algorithms · doi:10.3390/a10040109 · Cited by 29

Topic specific emotion detection for retweet prediction

Firdaus, S. N., Ding, C., & Sadeghian, A. (2018) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-018-0798-5 · Cited by 30

User emotion analysis in conflicting versus non-conflicting regions using online social networks

Wani, M. A., Agarwal, N., Jabin, S., & Hussain, S. Z. (2018) · Telematics and Informatics · doi:10.1016/j.tele.2018.09.012 · Cited by 30

Use of microblogging platform for digital communication in politics

Paul, J., Parameswar, N., Sindhani, M., & Dhir, S. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2021.01.046 · Cited by 31

TRCM: A Methodology for Temporal Analysis of Evolving Concepts in Twitter

Adedoyin-Olowe, M., Gaber, M. M., & Stahl, F. T. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38610-7_13 · Cited by 29

Mapping Temporal Horizons

Jatowt, A., Antoine, É., Kawai, Y., & Akiyama, T. (2015) · International World Wide Web Conference (WWW) · doi:10.1145/2736277.2741632 · Cited by 29

Linked knowledge sources for topic classification of microposts: A semantic graph-based approach

Varga, A. E., Basave, A. E. C., Rowe, M., Ciravegna, F., & He, Y. (2014) · Journal of Web Semantics · doi:10.1016/j.websem.2014.04.001 · Cited by 29

Improving Sentiment Analysis of Short Informal Indonesian Product Reviews using Synonym Based Feature Expansion

Fauzi, M. A., Firmansyah, R. F. N., & Afirianto, T. (2018) · TELKOMNIKA (Telecommunication Computing Electronics and Control) · doi:10.12928/telkomnika.v16i2.7751 · Cited by 29

Going Back in Time to Predict the Future - The Complex Role of the Data Collection Period in Social Media Analytics

Stieglitz, S., Meske, C., Roß, B., & Mirbabaie, M. (2018) · Information Systems Frontiers · doi:10.1007/s10796-018-9867-2 · Cited by 32

Visualising Business Data: A Survey

Roberts, R. C., & Laramee, R. S. (2018) · Information · doi:10.3390/info9110285 · Cited by 29

Big Data Analysis of Volatility Spillovers of Brands across Social Media and Stock Markets

van Dieijen, M., Borah, A., Tellis, G. J., & Franses, P. H. (2019) · Industrial Marketing Management · doi:10.1016/j.indmarman.2018.12.006 · Cited by 32

Text mining and determinants of sentiments: Twitter social media usage by traditional media houses in Uganda

Namugera, F., Wesonga, R., & Jehopio, P. J. (2019) · Computational Social Networks · doi:10.1186/s40649-019-0063-4 · Cited by 29

Saving social media data: Understanding data management practices among social media researchers and their implications for archives

Hemphill, L., Hedstrom, M. L., & Leonard, S. (2020) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24368 · Cited by 36

Crowd Detection in Mass Gatherings Based on Social Media Data: A Case Study of the 2014 Shanghai New Year’s Eve Stampede

Duan, J., Zhai, W., & Cheng, C. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17228640 · Cited by 31

Sentiment Classification of Twitter Data Belonging to Saudi Arabian Telecommunication Companies

Qamar, A. M., Suliman, A., & Sohail, S. (2017) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2017.080150 · Cited by 29

Forecasting Nike's sales using Facebook data

Camilla, B. L., Vinothan, V., Florian, W., Melanie, S., Mats, E., Mukkamala, R. R., Lassen, N. B., Flesch, B., Hussain, A., & Vatrapu, R. (2016) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2016.7840881 · Cited by 29

Real-Time Sensing of Traffic Information in Twitter Messages

de Carvalho, S. F. L. (2010) · Cited by 29

Text‐based sentiment analysis in finance: Synthesising the existing literature and exploring future directions

Todd, A., Bowden, J., & Moshfeghi, Y. (2024) · Intelligent systems in accounting, finance and management/Intelligent systems in accounting, finance & management · doi:10.1002/isaf.1549 · Cited by 37

Multi-modal microblog classification via multi-task learning

Zhao, S., Yao, H., Zhao, S., Jiang, X., & Jiang, X. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2342-2 · Cited by 28

Prediction and analysis of Pakistan election 2013 based on sentiment analysis

Razzaq, M. A., Qamar, A. M., & Bilal, H. S. M. (2014) · Advances in Social Networks Analysis and Mining · doi:10.5555/3191835.3191972 · Cited by 28

Google Trends as a Predictor of Presidential Elections: The United States Versus Canada

Román, C. P., Gómez-Martínez, R., & Orden‐Cruz, C. (2020) · American Behavioral Scientist · doi:10.1177/0002764220975067 · Cited by 29

Movie Success Prediction Using Data Mining

Olubukola, D. A., Stephen, O. M., Funmilayo, A. K., Ayokunle, O., Akande, O., ODUROYE, A. P., Wumi, A., & M., Y. S. (2021) · British Journal of Computer Networking and Information Technology · doi:10.52589/bjcnit-cqocirec · Cited by 28

The Ripple Effect of Firm-Generated Content on New Movie Releases

Lu, S., Dinner, I. M., & Grewal, R. (2022) · Journal of Marketing Research · doi:10.1177/00222437221143066 · Cited by 28

What's in Twitter: I Know What Parties are Popular and Who You are Supporting Now!

Boutet, A., Kim, H., & Yoneki, E. (2012) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2012.32 · Cited by 28

Hashtag Recommendation Using Dirichlet Process Mixture Models Incorporating Types of Hashtags

Gong, Y., Zhang, Q., & Huang, X. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1046 · Cited by 28

Personalized Recommendation for Online Social Networks Information: Personal Preferences and Location-Based Community Trends

Khater, S., Gračanin, D., & Elmongui, H. G. (2017) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2017.2720632 · Cited by 28

Using Crowd-Source Based Features from Social Media and Conventional Features to Predict the Movies Popularity

Ahmed, M., Jahangir, M., Afzal, H., Majeed, A., & Siddiqi, I. (2015) · IEEE International Conference on Smart City/SocialCom/SustainCom · doi:10.1109/smartcity.2015.83 · Cited by 28

An Analysis of Online Twitter Sentiment Surrounding the European Refugee Crisis

Pope, D., & Griffith, J. (2016) · International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management · doi:10.5220/0006051902990306 · Cited by 28

INVESTIGATING CRIME-TO-TWITTER RELATIONSHIPS IN URBAN ENVIRONMENTS - FACILITATING A VIRTUAL NEIGHBORHOOD WATCH

Bendler, J., Brandt, T., Wagner, S., & Neumann, D. (2014) · Journal of the Association for Information Systems · Cited by 28

Mining social media: tracking content and predicting behavior

Tsagkias, M. (2012) · UvA-DARE (University of Amsterdam) · Cited by 30

Early warning analysis for social diffusion events

Colbaugh, R., & Glass, K. (2010) · IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi.2010.5484778 · Cited by 27

Stuttgart’s Black Thursday on Twitter: Mapping Political Protests with Social Media Data

Jungherr, A. M., & Jürgens, P. (2014) · Palgrave Macmillan UK eBooks · doi:10.1057/9781137276773_7 · Cited by 27

A Subword-Based Deep Learning Approach for Sentiment Analysis of Political Tweets

Pota, M., Esposito, M., Palomino, M. A., & Masala, G. L. (2018) · International Conference on Advanced Information Networking and Applications Workshops (WAINA) · doi:10.1109/waina.2018.00162 · Cited by 27

A study on the determinants of social media based learning in higher education

Rahman, T., Kim, Y. S., Noh, M., & Lee, C. K. (2021) · Educational Technology Research and Development · doi:10.1007/s11423-021-09987-2 · Cited by 27

Forecasting the box offices of movies coming soon using social media analysis: A method based on improved Bass models

Zhang, C., Tian, Y., & Fan, Z. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116241 · Cited by 27

AlBERTino for stock price prediction: a Gibbs sampling approach

Colasanto, F., Grilli, L., Santoro, D., & Villani, G. (2022) · Information Sciences · doi:10.1016/j.ins.2022.03.051 · Cited by 27

How to evaluate urban emotions using twitter social media?

Dorostkar, E., & Najarsadeghi, M. (2022) · Cities · doi:10.1016/j.cities.2022.103713 · Cited by 27

Introduction to Machine Learning

(2020) · Cambridge University Press eBooks · doi:10.1017/9781108690935.003 · Cited by 27

Behavior Informatics: A New Perspective

Cao, L., Joachims, T., Wang, C., Gaussier, É., Li, J., Ou, Y., Luo, D., Zafarani, R., Liu, H., Xu, G., et al. (2014) · IEEE Intelligent Systems · doi:10.1109/mis.2014.60 · Cited by 27

Factors Affecting the Current Diffusion of BIM: A Qualitative Study of Online Professional Network

Panuwatwanich, K., & Peansupap, V. (2013) · Griffith Research Online · Cited by 27

Can social microblogging be used to forecast intraday exchange rates?

Papaioannou, P., Russo, L., Papaioannou, G. P., & Siettos, C. I. (2013) · NETNOMICS Economic Research and Electronic Networking · doi:10.1007/s11066-013-9079-3 · Cited by 28

Understanding the user generated content and interactions on a Facebook brand page

Cvijikj, I. P., & Michahelles, F. (2013) · International Journal of Social and Humanistic Computing · doi:10.1504/ijshc.2013.053270 · Cited by 30

The effect of electronic word-of-mouth (eWOM) on mobile application downloads: an empirical investigation

Oh, S., Baek, H., & Ahn, J. (2015) · International Journal of Mobile Communications · doi:10.1504/ijmc.2015.067960 · Cited by 27

Collision Detection by Networked Sensors

Canzian, L., Demiryurek, U., & van der Schaar, M. (2015) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2015.2504721 · Cited by 27

Predictive value of video-sharing behavior: sharing of movie trailers and box-office revenue

Oh, S., Baek, H., & Ahn, J. (2017) · Internet Research · doi:10.1108/intr-01-2016-0005 · Cited by 27

Predicting movie box office success using multiple regression and SVM

Subramaniyaswamy, V., Vaibhav, M. V., Prasad, R. V., & Ravi, L. (2017) · International Conference on Intelligent Sustainable Systems (ICISS) · doi:10.1109/iss1.2017.8389394 · Cited by 27

Exploring Wine Terroir Experiences: A Social Media Analysis

Kastenholz, E., Cunha, D., Eletxigerra, A., Carvalho, M., & Silva, I. (2020) · Smart innovation, systems and technologies · doi:10.1007/978-981-33-4260-6_35 · Cited by 27

Agenda Trending: Reciprocity and the Predictive Capacity of Social Network Sites in Intermedia Agenda Setting across Issues over TimePreprint

Groshek, J., & Groshek, M. C. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2199144 · Cited by 27

Towards a Set Theoretical Approach to Big Data Analytics

Mukkamala, R. R., Hussain, A., & Vatrapu, R. (2014) · IEEE International Congress on Big Data (BigData Congress) · doi:10.1109/bigdata.congress.2014.96 · Cited by 25

The Growing Role of Social Media in International Health Security: The Good, the Bad, and the Ugly

Stawicki, S. P., Firstenberg, M. S., & Papadimos, T. J. (2020) · Advanced sciences and technologies for security applications · doi:10.1007/978-3-030-23491-1_14 · Cited by 26

Pre-release Box-Office Success Prediction for Motion Pictures

Parimi, R., & Caragea, D. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39712-7_44 · Cited by 26

Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations

Shah, Z., Martin, P. B., Coiera, E. W., Mandl, K. D., & Dunn, A. G. (2019) · Journal of Medical Internet Research · doi:10.2196/12881 · Cited by 27

Analyzing, Classifying, and Interpreting Emotions in Software Users' Tweets

Williams, G., & Mahmoud, A. (2017) · IEEE/ACM International Workshop on Emotion Awareness in Software Engineering (SEmotion) · doi:10.1109/semotion.2017.1 · Cited by 24

Nowcasting commodity prices using social media

Kim, J., Cha, M., & Lee, J. G. (2017) · PeerJ Computer Science · doi:10.7717/peerj-cs.126 · Cited by 25

Real-Time Top-R Topic Detection on Twitter with Topic Hijack Filtering

Hayashi, K., Maehara, T., Toyoda, M., & Kawarabayashi, K. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783402 · Cited by 25

Exploring the "Twitter Effect:" An Investigation of the Impact of Microblogging Word of Mouth on Consumers' Early Adoption of New Products

Hennig‐Thurau, T., & Wiertz, C. (2012) · Cited by 25

Measuring, Predicting and Visualizing Short-Term Change in Word Representation and Usage in VKontakte Social Network

Stewart, I., Arendt, D., Bell, E., & Volkova, S. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14938 · Cited by 25

A review on web content popularity prediction: Issues and open challenges

Moniz, N., & Torgo, L. (2019) · Online Social Networks and Media · doi:10.1016/j.osnem.2019.05.002 · Cited by 25

Toward Socially-Infused Information Extraction: Embedding Authors, Mentions, and Entities

Yang, Y., Chang, M.-W., & Eisenstein, J. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1152 · Cited by 25

Are students happier the more they learn? – Research on the influence of course progress on academic emotion in online learning

Pan, X., Hu, B., Zhou, Z., & Feng, X. (2022) · Interactive Learning Environments · doi:10.1080/10494820.2022.2052110 · Cited by 31

Social Media in the African Context

Bhanye, J. I., Shayamunda, R. H., & Tavirai, R. C. (2023) · Springer eBooks · doi:10.1007/978-3-030-68127-2_366-1 · Cited by 27

A Sentiment Analysis of Twitter Content as a Predictor of Exchange Rate Movements

Öztürk, S. S., & Ciftci, K. (2015) · Review of Economic Analysis · doi:10.15353/rea.v6i2.1416 · Cited by 26

How significant are users’ opinions in social media?

Khobzi, H., & Teimourpour, B. (2014) · International Journal of Accounting and Information Management · doi:10.1108/ijaim-02-2014-0010 · Cited by 25

Multi-source models for civil unrest forecasting

Korkmaz, G., Cadena, J., Kuhlman, C. J., Marathe, A., Vullikanti, A. K. S., & Ramakrishnan, N. (2016) · Social Network Analysis and Mining · doi:10.1007/s13278-016-0355-8 · Cited by 25

Application of Lexicon Based Approach in Sentiment Analysis for short Tweets

Agarwal, A., & Toshniwal, D. (2018) · International Conference on Advances in Computing and Communication Engineering · doi:10.1109/icacce.2018.8441696 · Cited by 25

A machine learning analysis of Twitter sentiment to the Sandy Hook shootings

Wang, N., Varghese, B., & Donnelly, P. (2016) · IEEE International Conference on eScience · doi:10.1109/escience.2016.7870913 · Cited by 25

Popularity Prediction for Single Tweet Based on Heterogeneous Bass Model

Gao, X., Zheng, Z., Chu, Q., Tang, S., Chen, G., & Deng, Q. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2952856 · Cited by 25

Emotional Reactions and the Pulse of Public Opinion: Measuring the Impact of Political Events on the Sentiment of Online DiscussionsPreprint

González‐Bailón, S., Banchs, R. E., & Kaltenbrunner, A. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1009.4019 · Cited by 34

Text mining and social media: when quantitative meets qualitative, and software meets humans

Ampofo, L., Collister, S., O’Loughlin, B., & Chadwick, A. (2015) · University of the Arts London Research Online (University of the Arts London) · Cited by 24

Style in the Age of Instagram: Predicting Success within the Fashion Industry using Social Media

Park, J., Ciampaglia, G. L., & Ferrara, E. (2015) · KDI Central Archives (Korea Development Institute) · doi:10.48550/arxiv.1508.04185 · Cited by 24

Are Twitter Users Equal in Predicting Elections? A Study of User Groups in Predicting 2012 U.S. Republican Presidential Primaries

Chen, L., Wang, W., & Sheth, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35386-4_28 · Cited by 24

Using Machine Learning to Improve Lead Times in the Identification of Emerging Customer Needs

Kilroy, D. P., Healy, G. F., & Caton, S. J. (2022) · IEEE Access · doi:10.1109/access.2022.3165043 · Cited by 24

Overcoming Data Scarcity of Twitter

Beykikhoshk, A., Arandjelović, O., Phung, D., & Venkatesh, S. (2015) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/2808797.2808908 · Cited by 32

Online Social Networks Flu Trend Tracker: A Novel Sensory Approach to Predict Flu Trends

Achrekar, H., Gandhe, A., Lazarus, R. M., Yu, S.-H., & Liu, B. (2013) · Communications in computer and information science · doi:10.1007/978-3-642-38256-7_24 · Cited by 24

Data analysis across various media: Data fusion, direct marketing, clickstream data and social media

Breur, T. (2011) · Journal of Direct Data and Digital Marketing Practice · doi:10.1057/dddmp.2011.32 · Cited by 24

Competition for popularity in bipartite networks

Beguerisse-Díaz, M., Porter, M. A., & Onnela, J.-P. (2010) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.3475411 · Cited by 24

The Dual Micro/Macro Informing Role of Social Network Sites: Can Twitter Macro Messages Help Predict Stock Prices?

Evangelopoulos, N. E., Magro, M. J., & Sidorova, A. (2012) · Informing Science The International Journal of an Emerging Transdiscipline · doi:10.28945/1739 · Cited by 23

Predicting Movie Box Office Profitability: A Neural Network Approach

Ginmu, R. T., & Zulkernine, F. H. (2016) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2016.0117 · Cited by 24

Identifying long-term periodic cycles and memories of collective emotion in online social media

Sano, Y., Hideki Takayasu, C., Havlin, S., & Takayasu, M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0213843 · Cited by 28

Internet penetration and international travel and tourism expenditure: The role of foreign exchange control

Lorente-Bayona, L. V., Gil, E. G., & Moreno‐Enguix, M. D. R. (2021) · Tourism Economics · doi:10.1177/13548166211027839 · Cited by 24

Towards Decision Support and Goal Achievement: Identifying Action-Outcome Relationships From Social Media

Kıcıman, E., & Richardson, M. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783310 · Cited by 23

Surpassing the Limit

Sampson, J., Morstatter, F., Maciejewski, R., & Liu, H. (2015) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/2700171.2791030 · Cited by 24

Hoax News Detection on Twitter using Term Frequency Inverse Document Frequency and Support Vector Machine Method

Fauzi, A. R., Setiawan, E. B., & Baizal, Z. K. A. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1192/1/012025 · Cited by 23

Text mining social media for competitive analysis

Gémar, G., & Jiménez-Quintero, J. A. (2015) · Dialnet (Universidad de la Rioja) · Cited by 23

Analyzing sentimental influence of posts on social networks

Sun, B., & Ng, V. (2014) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd.2014.6846903 · Cited by 23

Ant colony based approach to predict stock market movement from mood collected on Twitter

Bouktif, S., & Awad, M. (2013) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/2492517.2500282 · Cited by 23

Sentiment-Based Features for Predicting Election Polls: A Case Study on the Brazilian Scenario

Tumitan, D., & Becker, K. E. (2014) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat.2014.89 · Cited by 23

Group-based Latent Dirichlet Allocation (Group-LDA): Effective audience detection for books in online social media

Zhang, P., Gu, H., Gartrell, M., Lu, T., Yang, D., Ding, X., & Gu, N. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.05.006 · Cited by 23

A graph-oriented model for hierarchical user interest in precision social marketing

Zhu, Z., Zhou, Y., Deng, X., & Wang, X. (2019) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2019.100845 · Cited by 23

Mobility Network Evaluation in the User Perspective: Real-Time Sensing of Traffic Information in Twitter Messages

Kokkinogenis, Z., Filguieras, J., Carvalho, S. B., Sarmento, L., & Rossetti, R. J. F. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397041-1.00012-1 · Cited by 23

Audiencia social en Twitter: hacia un nuevo modelo de consumo televisivo

Claes, F., & Deltell, L. (2025) · Tripodos · doi:10.51698/tripodos.2015.36.109-132 · Cited by 22

Propagation-based Sentiment Analysis for Microblogging Data

Tang, J., Nobata, C., Dong, A., Chang, Y., & Liu, H. (2015) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611974010.65 · Cited by 22

Changes in Public Response Associated With Various COVID-19 Restrictions in Ontario, Canada: Observational Infoveillance Study Using Social Media Time Series Data

Chum, A., Nielsen, A., Bellows, Z., Farrell, E., Durette, P.-N., Banda, J. M., & Cupchik, G. C. (2021) · Journal of Medical Internet Research · doi:10.2196/28716 · Cited by 22

A survey on the use of data and opinion mining in social media to political electoral outcomes prediction

dos Santos, J. S., Bernardini, F., & Paes, A. (2021) · Social Network Analysis and Mining · doi:10.1007/s13278-021-00813-4 · Cited by 22

Opinion Dynamics of Elections in Twitter

Bravo-Márquez, F., Gayo-Avello, D., Mendoza, M., & Poblete, B. (2012) · Latin American Web Congress · doi:10.1109/la-web.2012.11 · Cited by 22

Detecting and making use of emotions to enhance student motivation in e-learning environments

Rodrı́guez, P., Ortigosa, Á., & Carro, R. M. (2014) · International Journal of Continuing Engineering Education and Life-Long Learning · doi:10.1504/ijceell.2014.060156 · Cited by 22

Crossing Media Streams with Sentiment: Domain Adaptation in Blogs, Reviews and Twitter

Mejova, Y., & Srinivasan, P. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v6i1.14242 · Cited by 22

Prediction of places of visit using tweets

Chauhan, A., Kummamuru, K., & Toshniwal, D. (2016) · Knowledge and Information Systems · doi:10.1007/s10115-016-0936-x · Cited by 22

Quality management architecture for social media data

Pääkkönen, P., & Jokitulppo, J. (2017) · Journal Of Big Data · doi:10.1186/s40537-017-0066-7 · Cited by 22

Pacts with Twitter. Predicting voters' indecision and preferences for coalitions in multiparty systems

Guerrero-Solé, F., Corominas‐Murtra, B., & López-González, H. (2014) · Information Communication & Society · doi:10.1080/1369118x.2014.920040 · Cited by 24

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

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