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

Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysisPreprint

Beykikhoshk, A., Arandjelović, O., Phung, D., & Venkatesh, S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.02973 · Cited by 22

CorMulT: A Semi-Supervised Modality Correlation-Aware Multimodal Transformer for Sentiment Analysis

Li, Y., Zhu, R., & Li, W. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3559866 · Cited by 21

USING STARTUP COMMUNICATION FOR OPPORTUNITY RECOGNITION — AN APPROACH TO IDENTIFY FUTURE PRODUCT TRENDS

SIMON, H., & Leker, J. (2016) · International Journal of Innovation Management · doi:10.1142/s1363919616400168 · Cited by 21

Artificial intelligence-based predictions of movie audiences on opening Saturday

An, Y., An, J., & Cho, S. (2020) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2020.05.005 · Cited by 21

Effects of PM2.5 on People’s Emotion: A Case Study of Weibo (Chinese Twitter) in Beijing

Shan, S., Ju, X., Wei, Y., & Wang, Z. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18105422 · Cited by 21

Panic buying and fake news in urban vs. rural England: A case study of twitter during COVID-19

Ali, M., Gomes, L. M., Azab, N., Souza, J. G. D. M., Sorour, M. K., & Kimura, H. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122598 · Cited by 22

Box-Office Opening Prediction of Movies based on Hype Analysis through Data Mining

SivaSantoshReddy, A., Kasat, P., & Jain, A. (2012) · International Journal of Computer Applications · doi:10.5120/8852-2794 · Cited by 21

Privacy-Preserving and Efficient Friend Recommendation in Online Social Networks

Samanthula, B. K., Cen, L., Jiang, W., & Si, L. (2015) · Transactions on data privacy · doi:10.5555/2870559.2870562 · Cited by 21

Examining the existence of double jeopardy and negative double jeopardy within Twitter

Rogers, A., Daunt, K. L., Morgan, P., & Beynon, M. J. (2017) · European Journal of Marketing · doi:10.1108/ejm-03-2015-0126 · Cited by 21

Measuring Employment Demand Using Internet Search Data

Chancellor, S., & Counts, S. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173696 · Cited by 21

Harnessing aspect-based sentiment analysis: How are tweets associated with forecast accuracy?

The Australian National University, A., Ho, S. Y., Choi, K. W. (., The Australian National University, A., Yang, F. (., & The Australian National University, A. (2019) · Journal of the Association for Information Systems · doi:10.17705/1jais.00564 · Cited by 21

The dynamic evolution of social ties and user-generated content: a case study on a Douban group

Shan, S., Ren, J., & Li, C. (2016) · Enterprise Information Systems · doi:10.1080/17517575.2016.1177204 · Cited by 25

The Impact of Emotion: A Blended Model to Estimate Influence on Social Media

Chang, W. (2018) · Information Systems Frontiers · doi:10.1007/s10796-018-9824-0 · Cited by 21

WeChat usage during COVID-19 pandemic lockdown: the mediating role of online self-disclosure on quality of friendship and well-being

Amosun, T. S., Jianxun, C., Rufai, O. H., Muhideen, S., Shahani, R., Shah, Z., & Koroma, J. (2022) · Global Knowledge, Memory and Communication · doi:10.1108/gkmc-09-2020-0136 · Cited by 20

The End of the Search Engine Era and the Rise of Generative AI: A Paradigm Shift in Information Retrieval

Amer, E., & Elboghdadly, T. (2024) · International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) · doi:10.1109/miucc62295.2024.10783559 · Cited by 28

Do App Launch Times Impact their Subsequent Commercial Success? An Analytical Approach

Datta, D., & Sangaralingam, K. (2013) · doi:10.1109/cloudcom-asia.2013.56 · Cited by 20

A Large Scale Study to Understand the Relation between Twitter and Financial Market

Cazzoli, L., Sharma, R., Treccani, M., & Lillo, F. (2016) · European Network Intelligence Conference (ENIC) · doi:10.1109/enic.2016.022 · Cited by 20

Mining Twitter data for causal links between tweets and real-world outcomes

Lim, S., & Tucker, C. S. (2019) · Expert Systems with Applications: X · doi:10.1016/j.eswax.2019.100007 · Cited by 14

Can Sentiment Analysis and Options Volume Anticipate Future Returns?

Houlihan, P., & Creamer, G. G. (2017) · Computational Economics · doi:10.1007/s10614-017-9694-4 · Cited by 20

Verbal sentiment analysis and detection using recurrent neural network

Mohanty, M. D., & Mohanty, M. N. (2022) · Elsevier eBooks · doi:10.1016/b978-0-32-385708-6.00012-6 · Cited by 20

Impact of Tweets on Box Office Revenue: Focusing on When Tweets are Written

Baek, H., Ahn, J., & Oh, S. (2014) · ETRI Journal · doi:10.4218/etrij.14.0113.0732 · Cited by 20

Semantically driven social data aggregation interfaces for Research 2.0

Vocht, L. D., Softic, S., Ebner, M., & Mühlburger, H. (2011) · International Conference on Knowledge Management and Knowledge Technologies · doi:10.1145/2024288.2024339 · Cited by 20

A Study of Sentiment and Trend Analysis Techniques for Social Media Content

Mehmood, A., Palli, A. S., & Khan, M. N. A. (2014) · International Journal of Modern Education and Computer Science · doi:10.5815/ijmecs.2014.12.07 · Cited by 20

Webometrics benefitting from web mining? An investigation of methods and applications of two research fields

Lorentzen, D. G. (2014) · Scientometrics · doi:10.1007/s11192-013-1227-x · Cited by 20

Interplay between Public Attention and Public Emotion toward Multiple Social Issues on Twitter

Peng, T., Sun, G., & Wu, Y. (2017) · PLoS ONE · doi:10.1371/journal.pone.0167896 · Cited by 22

The importance of multimodality in sarcasm detection for sentiment analysis

Razali, M. S., Halin, A. B. A., Norowi, N. M., & Doraisamy, S. C. (2017) · IEEE Student Conference on Research and Development (SCOReD) · doi:10.1109/scored.2017.8305421 · Cited by 20

Big data would not lie: prediction of the 2016 Taiwan election via online heterogeneous information

Xie, Z., Liu, G., Wu, J., & Tan, Y. (2018) · EPJ Data Science · doi:10.1140/epjds/s13688-018-0163-7 · Cited by 21

User interest prediction over future unobserved topics on social networks

Zarrinkalam, F., Kahani, M., & Ebrahim Bagheri, A. (2018) · Information Retrieval · doi:10.1007/s10791-018-9337-y · Cited by 20

Language, brand and speech acts on Twitter

Chandler, J. D., Salvador, R. O., & Kim, Y. (2018) · Journal of Product & Brand Management · doi:10.1108/jpbm-06-2017-1493 · Cited by 20

Predicting the spread of influenza epidemics by analyzing twitter messages

Molaei, S., Khansari, M., Veisi, H., & Salehi, M. (2019) · Health and Technology · doi:10.1007/s12553-019-00309-4 · Cited by 20

Movie Success Prediction Using ML

Darapaneni, N., Bellarmine, C., Paduri, A. R., Entoori, S., Kumar, A., Vybhav, S. V., & Mondal, K. (2020) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon51285.2020.9298145 · Cited by 20

Directional prediction of stock prices using breaking news on Twitter

Alostad, H., & Davulcu, H. (2017) · Web Intelligence · doi:10.3233/web-170349 · Cited by 20

Need for Speed: An Empirical Analysis of Hard and Soft Information in a High Frequency WorldPreprint

Zhang, S. S. (2012) · SSRN Electronic Journal · doi:10.2139/ssrn.1985951 · Cited by 20

Named Entity Recognition and Hashtag Decomposition to Improve the Classification of Tweets

Belainine, B., Fonseca, A., & Sadat, F. (2016) · International Conference on Computational Linguistics · Cited by 19

Fikih Informasi : Muhammadiyah’s Perspective on Guidance in Using Social Media

Fauzi, N. A. F., & Ayub, A. (2019) · Indonesian Journal of Islam and Muslim Societies · doi:10.18326/ijims.v9i2.267-293 · Cited by 31

Deep Exogenous and Endogenous Influence Combination for Social Chatter Intensity Prediction

Dutta, S., Masud, S., Chakrabarti, S., & Chakraborty, T. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403251 · Cited by 18

Does the Adaptive Market Hypothesis Reconcile the Behavioral Finance and the Efficient Market Hypothesis?

Noreen, U., Shafique, A., Ayub, U., & Saeed, S. K. (2022) · Risks · doi:10.3390/risks10090168 · Cited by 24

Can you feel the advertisement tonight? The effect of ASMR cues in video advertising on purchase intention

Kerpel, L. D., Kerckhove, A. V., & Tessitore, T. (2023) · International Journal of Advertising · doi:10.1080/02650487.2023.2262328 · Cited by 20

Early Prediction of Movie Success — What, Who, and When

Lash, M. T., Fu, S., Wang, S., & Zhao, K. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16268-3_41 · Cited by 19

A tale of three graphs: Sampling design on hybrid social-affiliation networks

Zhao, J., Lui, J. C. S., Towsley, D., Wang, P., & Guan, X. (2015) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2015.7113346 · Cited by 19

Social media adoption in disaster restoration industry

Pan, J., Vorvoreanu, M., & Zhou, Z. (2014) · Construction Innovation · doi:10.1108/ci-03-2013-0014 · Cited by 19

An automated approach to quantifying functional interactions by mining large-scale product specification data

Kang, S. W., & Tucker, C. S. (2015) · Journal of Engineering Design · doi:10.1080/09544828.2015.1083539 · Cited by 19

Two information aggregation mechanisms for predicting the opening weekend box office revenues of films: Boxoffice Prophecy and Guess of Guesses

Court, D., Gillen, B. J., McKenzie, J., & Plott, C. R. (2017) · Economic Theory · doi:10.1007/s00199-017-1036-1 · Cited by 19

Extraction of Semantic Activities from Twitter Data.

Panasyuk, A. V., Blasch, E., Kase, S. E., & Bowman, L. (2013) · Cited by 19

The influence of social media on stock volatility

Wu, X., Wang, X., Ma, S., & Ye, Q. (2017) · Frontiers of Engineering Management · doi:10.15302/j-fem-2017018 · Cited by 22

Using Twitter Language to Predict the Real Estate Market

Zamani, M., & Schwartz, H. A. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-2005 · Cited by 21

Grounded emotions

Liu, V., Banea, C., & Mihalcea, R. F. (2017) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2017.8273642 · Cited by 19

How efficient is Twitter: Predicting 2012 U.S. presidential elections using Support Vector Machine via Twitter and comparing against Iowa Electronic Markets

Attarwala, A., Dimitrov, S., & Obeidi, A. (2017) · Intelligent Systems Conference (IntelliSys) · doi:10.1109/intellisys.2017.8324363 · Cited by 19

SOSYAL MEDYA ANALİTİĞİ İLE DEĞER YARATMA: DUYGU ANALİZİ İLE GELECEĞE YÖNELİM

Burcu, A. K. I. N., & Gursoy, U. T. (2018) · Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi · doi:10.30798/makuiibf.435804 · Cited by 19

Hybrid features prediction model of movie quality using Multi-machine learning techniques for effective business resource planning

Iqbal, N., Ahmad, R., Jamil, F., & Kim, D. (2021) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-201844 · Cited by 19

Using data mining techniques to fight and control epidemics: A scoping review

Safdari, R., Rezayi, S., Saeedi, S., Tanhapour, M., & Gholamzadeh, M. (2021) · Health and Technology · doi:10.1007/s12553-021-00553-7 · Cited by 20

Using Sentiment Analysis for Stock Exchange Prediction

L.Lima, M., Nascimento, T. P., Labidi, S., Timbo, N. S., Batista, M. V. L., Neto, G. N., Costa, E. A. M., & Sousa, S. R. S. (2016) · International Journal of Artificial Intelligence & Applications · doi:10.5121/ijaia.2016.7106 · Cited by 19

Opinion Retrieval: Searching for Opinions in Social Media

Paltoglou, G., & Giachanou, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12511-4_10 · Cited by 19

Sentiment analysis for the Japanese stock market

Ishijima, H., Kazumi, T., & Maèda, A. (2015) · Global Business and Economics Review · doi:10.1504/gber.2015.070303 · Cited by 19

Belief Dynamics in Social Networks: A Fluid-Based Analysis

Nordio, A., Tarable, A., Chiasserini, C. F., & Leonardi, E. (2017) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2017.2760016 · Cited by 19

The Promise of Strategic Customer Behavior: On the Value of Click TrackingPreprint

Huang, T., & Mieghem, J. A. V. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1625602 · Cited by 19

Belief Dynamics in Social Networks: A Fluid-Based Analysis

Nordio, A., Tarable, A., Chiasserini, C. F., & Leonardi, E. (n.d.) · PORTO Publications Open Repository TOrino (Politecnico di Torino) · Cited by 19

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) · doi:10.12924/mac2013.01010015 · Cited by 19

A Model for Prediction of Human Depression Using Apriori Algorithm

Jena, L., & Kamila, N. K. (2014) · doi:10.1109/icit.2014.65 · Cited by 18

D-Bees: A novel method inspired by bee colony optimization for solving word sense disambiguation

Abualhaija, S., & Zimmermann, K.-H. (2016) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2015.12.002 · Cited by 18

Sentiment analysis of Twitter texts using Machine learning algorithms

BARZENJI, H. (2021) · Academic Platform Journal of Engineering and Smart Systems · doi:10.21541/apjes.939338 · Cited by 20

Social Media Impact on Holiday Travel Planning

Fotis, J., Buhalis, D., & Rossides, N. (2012) · IGI Global eBooks · doi:10.4018/978-1-4666-1861-9.ch016 · Cited by 18

Predicting Facebook sentiments towards research

Shahzad, M., Freeman, C., Rahimi, M., & Alhoori, H. (2023) · Natural Language Processing Journal · doi:10.1016/j.nlp.2023.100010 · Cited by 21

Sentiment Analysis for Government: An Optimized Approach

Corallo, A., Fortunato, L., Matera, M., Alessi, M., Camillò, A., Chetta, V., Giangreco, E., & Storelli, D. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-21024-7_7 · Cited by 18

Interactive Sensing and Decision Making in Social Networks

Krishnamurthy, V. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988133 · Cited by 19

Twitter Sentiment Analysis for Security-Related Information Gathering

Jurek, A., Bi, Y., & Mulvenna, M. D. (2014) · doi:10.1109/jisic.2014.17 · Cited by 18

SOCIO-SEMANTIC FRAMEWORKS

Roth, C. (2013) · Advances in Complex Systems · doi:10.1142/s0219525913500136 · Cited by 18

Where There is a Sea There are Pirates: Response to Jungherr, Jürgens, and Schoen

Tumasjan, A., Sprenger, T. O., Sandner, P., & Welpe, I. M. (2011) · Social Science Computer Review · doi:10.1177/0894439311404123 · Cited by 18

HOW SOCIAL COMMUNITY AND SOCIAL PUBLISHING INFLUENCE NEW PRODUCT LAUNCH: THE CASE OF TWITTER DURING THE PLAYSTATION 4 AND XBOX ONE LAUNCHES

Kim, Y., & Chandler, J. D. (2018) · The Journal of Marketing Theory and Practice · doi:10.1080/10696679.2017.1389238 · Cited by 19

A Tale of Two Movements: Egypt During the Arab Spring and Occupy Wall Street

Al-Hasan, A., Yim, D., & Lucas, H. C. (2018) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2018.2812194 · Cited by 22

Application of soft computing techniques for estimating emotional states expressed in Twitter® time series data

Çakıt, E., Karwowski, W., & Servi, L. D. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04048-5 · Cited by 18

An Ensemble Approach to Utterance Level Multimodal Sentiment Analysis

Huddar, M. G., Sannakki, S. S., & Rajpurohit, V. S. (2018) · International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS) · doi:10.1109/ctems.2018.8769162 · Cited by 18

Coupled propagation dynamics on multiplex activity-driven networks

Hu, P., Geng, D., Lin, T., & Ding, L. (2020) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2020.125212 · Cited by 18

Utilizing a multi-class classification approach to detect therapeutic and recreational misuse of opioids on Twitter

Fodeh, S. J., Al-Garadi, M. A., Elsankary, O., Perrone, J., Becker, W. C., & Sarker, A. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104132 · Cited by 17

Measuring Corporate Reputation using Sentiment Analysis

Colleoni, E., Arvidsson, A., Hansen, L. K., & Marchesini, A. (2011) · OpenArchive@CBS (Copenhagen Business School) · Cited by 18

Popularity Prediction in Microblogging Network

Gao, S., Ma, J., & Chen, Z. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11116-2_33 · Cited by 18

Circadian Patterns in Twitter

ten Thij, M., Kampstra, P., & Bhulai, S. (2014) · Cited by 17

Social media popularity and election results: A study of the 2016 Taiwanese general election

Zhang, X. (2018) · PLoS ONE · doi:10.1371/journal.pone.0208190 · Cited by 18

Non-parametric causality detection: An application to social media and financial data

Tsapeli, F., Musolesi, M., & Peter Tiňo, B. (2017) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2017.04.101 · Cited by 19

Using Twitter to Predict Sales: A Case StudyPreprint

Dijkman, R., Ipeirotis, P. G., Aertsen, F., & van Helden, R. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1503.04599 · Cited by 18

Mobile behaviometric framework for sociability assessment and identification of smartphone users

Anjomshoa, F., Catalfamo, M., Hecker, D., Nicklaus, H., Andrew, R., Kantarcı, B., Erol‐Kantarci, M., & Schuckers, S. (2016) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc.2016.7543880 · Cited by 17

Do social media create revolutions? Using Twitter sentiment analysis for predicting the Maidan Revolution in Ukraine

Sabatovych, I. (2019) · Global Media and Communication · doi:10.1177/1742766519872780 · Cited by 17

Social media analysis and summarization for opinion mining: a business case study

Rahmani, A., Chen, A., Sarhan, A., Jida, J., Rifaie, M., & Alhajj, R. (2014) · Social Network Analysis and Mining · doi:10.1007/s13278-014-0171-y · Cited by 17

Twitter volume spikes

Mao, Y., Wei, W., & Wang, B. (2013) · Social Network Mining and Analysis · doi:10.1145/2501025.2501039 · Cited by 17

Social set analysis

Vatrapu, R., Hussain, A., Lassen, N. B., Mukkamala, R. R., Flesch, B., & Madsen, R. (2015) · doi:10.1145/2789187.2789203 · Cited by 17

An algorithm based on Google Trends' data for future prediction. Case study: German elections

Polykalas, S. E., Prezerakos, G. N., & Konidaris, A. (2013) · IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) · doi:10.1109/isspit.2013.6781856 · Cited by 17

Identifying Themes in Social Media and Detecting Sentiments

Pal, J., & Saha, A. (2010) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2010.25 · Cited by 17

Measuring Propagation in Online Social Networks: The Case of YouTube

Rad, A. A., & Benyoucef, M. (2011) · Cited by 17

The predictive power of the business and bank sentiment of firms: A high-dimensional Granger Causality approach

Wilms, I., Gelper, S., & Croux, C. (2016) · European Journal of Operational Research · doi:10.1016/j.ejor.2016.03.041 · Cited by 17

Electoral Predictions with Twitter: A Machine-Learning approach

Coletto, M., Lucchese, C., Orlando, S., & Perego, R. (2015) · IMT Institutional Repository (IMT School for Advanced Studies Lucca) · Cited by 17

Does Twitter chatter matter? Online reviews and box office revenues

Vujić, S., & Zhang, X. (2018) · Applied Economics · doi:10.1080/00036846.2018.1436148 · Cited by 17

#EULAR2018: The Annual European Congress of Rheumatology—a Twitter hashtag analysis

Negrón, J. B. (2019) · Rheumatology International · doi:10.1007/s00296-019-04249-0 · Cited by 17

Good Things Come to Those Who Binge: An Exploration of Binge-Watching Related Behavior

Boca, P. (2019) · Journal of Media Research · doi:10.24193/jmr.34.1 · Cited by 17

Recurrent random forest for the assessment of popularity in social media

Tavazoee, F., Conversano, C., & Molà, F. (2019) · Knowledge and Information Systems · doi:10.1007/s10115-019-01410-w · Cited by 17

Let’s play on Facebook: using sentiment analysis and social media metrics to measure the success of YouTube gamers’ post types

Poecze, F., Ebster, C., & Strauß, C. (2019) · Personal and Ubiquitous Computing · doi:10.1007/s00779-019-01361-7 · Cited by 17

Emergent User Behavior on Twitter Modelled by a Stochastic Differential Equation

Mollgaard, A., & Mathiesen, J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0123876 · Cited by 17

Context-Sensitive Sentiment Classification of Short Colloquial Text

Blenn, N., Charalampidou, K., & Doerr, C. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30045-5_8 · Cited by 17

Learning to Predict from Textual Data

Radinsky, K., Davidovich, S., & Markovitch, S. (2012) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3865 · Cited by 47

Features for Ranking Tweets Based on Credibility and Newsworthiness

Ross, J., & Thirunarayan, K. (2016) · International Conference on Collaboration Technologies and Systems (CTS) · doi:10.1109/cts.2016.0023 · Cited by 17

Discovering evolving political vocabulary in social media

Mahendiran, A., Wang, W., Lira, J. A. S., Huang, B., Getoor, L. C., Mares, D. R., & Ramakrishnan, N. (2014) · International Conference on Behavioral, Economic, and Socio-Cultural Computing · doi:10.1109/besc.2014.7059504 · Cited by 17

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)