Cashtag Piggybacking: Uncovering Spam and Bot Activity in Stock Microblogs on Twitter

Twitter mood predicts the stock market

Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206 · see citing works

Groups of diverse problem solvers can outperform groups of high-ability problem solvers

Hong, L., & Page, S. E. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0403723101 · Cited by 2,103 · see citing works

The rise of social bots

Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016) · Communications of the ACM · doi:10.1145/2818717 · Cited by 1,973 · see citing works

Techniques and applications for sentiment analysis

Feldman, R. (2013) · Communications of the ACM · doi:10.1145/2436256.2436274 · Cited by 1,489 · see citing works

Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media

Chen, H., De, P., Hu, Y. (., & Hwang, B.-H. (2014) · Review of Financial Studies · doi:10.1093/rfs/hhu001 · Cited by 1,251 · see citing works

Weaponized health communication: Twitter bots and Russian trolls amplify the vaccine debate

Broniatowski, D. A., Jamison, A. M., Qi, S., AlKulaib, L., Chen, T., Benton, A., Quinn, S. C., & Dredze, M. (2018) · American Journal of Public Health · doi:10.2105/ajph.2018.304567 · Cited by 1,149 · see citing works

Social bots distort the 2016 U.S. Presidential election online discussion

Bessi, A., & Ferrara, E. (2016) · First Monday · doi:10.5210/fm.v21i11.7090 · Cited by 993 · see citing works

Social Media and Firm Equity Value

Luo, X., Zhang, J., & Duan, W. (2012) · Information Systems Research · doi:10.1287/isre.1120.0462 · Cited by 630 · see citing works

Detecting and Tracking Political Abuse in Social Media

Ratkiewicz, J., Conover, M., Meiss, M., Gonçalves, B., Flammini, A., & Menczer, F. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14127 · Cited by 580 · see citing works

oddball: Spotting Anomalies in Weighted Graphs

Akoglu, L., McGlohon, M., & Faloutsos, C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13672-6_40 · Cited by 545 · see citing works

Disinformation and social bot operations in the run up to the 2017 French presidential election

Ferrara, E. (2017) · First Monday · doi:10.5210/fm.v22i8.8005 · Cited by 524 · see citing works

The impact of social and conventional media on firm equity value: A sentiment analysis approach

Yu, Y., Duan, W., & Cao, Q. (2012) · Decision Support Systems · doi:10.1016/j.dss.2012.12.028 · Cited by 498 · see citing works

Fame for sale: Efficient detection of fake Twitter followers

Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.09.003 · Cited by 461 · cited 5× · see citing works

Hoaxy

Shao, C., Ciampaglia, G. L., Flammini, A., & Menczer, F. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872518.2890098 · Cited by 410 · see citing works

The Effects of Twitter Sentiment on Stock Price Returns

Ranco, G., Aleksovski, D., Caldarelli, G., Grčar, M., & Mozetič, I. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138441 · Cited by 395 · cited 1× · see citing works

The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race

Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2017) · Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion · doi:10.1145/3041021.3055135 · Cited by 381 · see citing works

Empirical evaluation and new design for fighting evolving Twitter spammers

Yang, C., Harkreader, R., & Gu, G. (2013) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2013.2267732 · Cited by 346 · see citing works

Understanding and combating link farming in the twitter social network

Ghosh, S., Viswanath, B., Kooti, F., Sharma, N. K., Korlam, G., Benevenuto, F., Ganguly, N., & Gummadi, K. P. (2012) · International World Wide Web Conference (WWW) · doi:10.1145/2187836.2187846 · Cited by 341 · see citing works

CopyCatch: stopping group attacks by spotting lockstep behavior in social networks

Beutel, A., Xu, W., Guruswami, V., Palow, C., & Faloutsos, C. (2013) · Proceedings of the 22nd international conference on World Wide Web · doi:10.1145/2488388.2488400 · Cited by 335 · see citing works

Universal features of correlated bursty behaviour

Karsai, M., Kaski, K. K., Barabási, A.-Ĺ., & Kertész, J. (2012) · Scientific Reports · doi:10.1038/srep00397 · Cited by 313 · see citing works

Widespread Worry and the Stock Market

Gilbert, É., & Karahalios, K. G. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14023 · Cited by 283 · see citing works

Correlating financial time series with micro-blogging activity

Ruiz, E. J., Hristidis, V., Castillo, C., Gionis, A., & Jaimes, A. (2012) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2124295.2124358 · Cited by 274 · see citing works

DeBot: Twitter Bot Detection via Warped Correlation

Chavoshi, N., Hamooni, H., & Mueen, A. (2016) · 2016 IEEE 16th International Conference on Data Mining (ICDM) · doi:10.1109/icdm.2016.0096 · Cited by 260 · see citing works

Sybillimit: a near-optimal social network defense against sybil attacks

Yu, H., Gibbons, P. B., Kaminsky, M., & Xiao, F. (2010) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2009.2034047 · Cited by 246 · see citing works

How Do Consumer Buzz and Traffic in Social Media Marketing Predict the Value of the Firm?

Luo, X., & Zhang, J. (2013) · Journal of Management Information Systems · doi:10.2753/mis0742-1222300208 · Cited by 227 · see citing works

Stream-based active learning for sentiment analysis in the financial domain

Smailović, J., Grćar, M., Nada Lavrač, A., & Žnidaršič, M. (2014) · Information Sciences · doi:10.1016/j.ins.2014.04.034 · Cited by 220 · see citing works

The power of prediction with social media

Schoen, H., Gayo-Avello, D., Metaxas, P., Mustafaraj, E., Strohmaier, M., & Gloor, P. A. (2013) · Internet Research · doi:10.1108/intr-06-2013-0115 · Cited by 217 · see citing works

WarningBird: A Near Real-Time Detection System for Suspicious URLs in Twitter Stream

Lee, S., & Kim, J. K. (2013) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2013.3 · Cited by 200 · see citing works

DNA-Inspired Online Behavioral Modeling and Its Application to Spambot Detection

Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2016) · IEEE Intelligent Systems · doi:10.1109/mis.2016.29 · Cited by 194 · cited 5× · see citing works

Social fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling

Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2017) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2017.2681672 · Cited by 168 · see citing works

SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News

Cortis, K., Freitas, A., Daudert, T., Huerlimann, M., Zarrouk, M., Handschuh, S., & Davis, B. (2017) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s17-2089 · Cited by 159 · see citing works

Socialbots: voices from the fronts

Hwang, T., Pearce, I., & Nanis, M. (2012) · INTR · doi:10.1145/2090150.2090161 · Cited by 131 · see citing works

Correlating S&P 500 stocks with Twitter data

Mao, Y., Wei, W., Wang, B., & Liu, B. (2012) · doi:10.1145/2392622.2392634 · Cited by 123 · see citing works

Could Social Bots Pose a Threat to Public Health?

Allem, J.-P., & Ferrara, E. (2018) · American Journal of Public Health · doi:10.2105/ajph.2018.304512 · Cited by 102 · see citing works

GLAD

Yu, R., He, X., & Liu, Y. (2015) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2811268 · Cited by 99 · see citing works

Japan's 2014 General Election: Political Bots, Right-Wing Internet Activism, and Prime Minister Shinzō Abe's Hidden Nationalist Agenda

Schäfer, F., Evert, S., & Heinrich, P. (2017) · Big Data · doi:10.1089/big.2017.0049 · Cited by 91 · see citing works

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 · see citing works

Catching Synchronized Behaviors in Large Networks: A Graph Mining Approach

Jiang, M., Cui, P., Beutel, A., Faloutsos, C., & Yang, S. (2016) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2746403 · Cited by 88 · see citing works

Vape, quit, tweet? Electronic cigarettes and smoking cessation on Twitter

van der Tempel, J., Noormohamed, A., Schwartz, R. M., Norman, C. D., Malas, M., & Zawertailo, L. A. (2016) · International Journal of Public Health · doi:10.1007/s00038-016-0791-2 · Cited by 87 · see citing works

Inferring Strange Behavior from Connectivity Pattern in Social Networks

Jiang, M., Cui, P., Beutel, A., Faloutsos, C., & Yang, S. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-06608-0_11 · Cited by 82 · see citing works

Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter

Clark, E. M., Jones, C. A., Williams, J. R., Kurti, A. N., Norotsky, M. C., Danforth, C. M., & Dodds, P. S. (2016) · PLoS ONE · doi:10.1371/journal.pone.0157304 · Cited by 75 · see citing works

Early filtering of ephemeral malicious accounts on Twitter

Lee, S., & Kim, J. K. (2014) · Computer Communications · doi:10.1016/j.comcom.2014.08.006 · Cited by 75 · see citing works

HoloScope

Liu, S., Hooi, B., & Faloutsos, C. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3133018 · Cited by 74 · see citing works

"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 · see citing works

Detection of Promoted Social Media Campaigns

Ferrara, E., Varol, O., Menczer, F., & Flammini, A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v10i1.14772 · Cited by 68 · see citing works

Manipulation and abuse on social mediaPreprint

Ferrara, E. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1503.03752 · Cited by 65 · see citing works

Learning Stock Market Sentiment Lexicon and Sentiment-Oriented Word Vector from StockTwits

Li, Q., & Shah, S. (2017) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k17-1031 · Cited by 56 · see citing works

On the capability of evolved spambots to evade detection via genetic engineering

Cresci, S., Petrocchi, M., Spognardi, A., & Tognazzi, S. (2018) · Online Social Networks and Media · doi:10.1016/j.osnem.2018.10.005 · Cited by 53 · see citing works

Retweeting Activity on Twitter: Signs of Deception

Γιάτσογλου, Μ., Chatzakou, D., Shah, N., Faloutsos, C., & Vakali, A. I. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-18038-0_10 · Cited by 49 · see citing works

Uncovering Fake Likers in Online Social Networks

Badri Satya, P. R., Lee, K., Lee, D., Tran, T., & Zhang, J. (. (2016) · Proceedings of the 25th ACM International on Conference on Information and Knowledge Management · doi:10.1145/2983323.2983695 · Cited by 46 · see citing works

Spotting Suspicious Behaviors in Multimodal Data: A General Metric and Algorithms

Jiang, M., Beutel, A., Cui, P., Hooi, B., Yang, S., & Faloutsos, C. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2555310 · Cited by 45 · see citing works

Strength in Numbers: Robust Tamper Detection in Crowd Computations

Viswanath, B., Bashir, M. A., Zafar, M. B., Bouget, S., Guha, S., Gummadi, K. P., Kate, A., & Mislove, A. (2015) · Proceedings of the 2015 ACM on Conference on Online Social Networks · doi:10.1145/2817946.2817964 · Cited by 43 · see citing works

Inferring lockstep behavior from connectivity pattern in large graphs

Jiang, M., Cui, P., Beutel, A., Faloutsos, C., & Yang, S. (2016) · Knowledge and Information Systems · doi:10.1007/s10115-015-0883-y · Cited by 43 · see citing works

Twitter sentiment around the Earnings Announcement events

Gabrovšek, P., Aleksovski, D., Mozetič, I., & Grćar, M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0173151 · Cited by 40 · see citing works

Overview of the Special Issue on Trust and Veracity of Information in Social Media

Papadopoulos, S., Bontcheva, K., Jaho, E., Lupu, M., & Castillo, C. (2016) · ACM Transactions on Information Systems · doi:10.1145/2870630 · Cited by 39 · see citing works

On using cashtags to predict companies stock trends

Bujari, A., Furini, M., & Laina, N. (2017) · IEEE Consumer Communications & Networking Conference (CCNC) · doi:10.1109/ccnc.2017.7983075 · Cited by 38 · see citing works

Detecting Spam and Promoting Campaigns in Twitter

Zhang, X., Li, Z., Zhu, S., & Liang, W. (2016) · ACM Transactions on the Web · doi:10.1145/2846102 · Cited by 36 · see citing works

Social Spammer and Spam Message Co-Detection in Microblogging with Social Context Regularization

Wu, F., Shu, J., Huang, Y., & Yuan, Z. (2015) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2806416.2806560 · Cited by 33 · see citing works

Nowcasting of Earthquake Consequences Using Big Social Data

Avvenuti, M., Cresci, S., Polla, M. N. L., Meletti, C., & Tesconi, M. (2017) · IEEE Internet Computing · doi:10.1109/mic.2017.4180834 · Cited by 32 · see citing works

Do Bots impact Twitter activity?

Gilani, Z., Farahbakhsh, R., & Crowcroft, J. (2017) · doi:10.1145/3041021.3054255 · Cited by 29 · see citing works

From Reaction to Proaction

Cresci, S., Petrocchi, M., Spognardi, A., & Tognazzi, S. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3191595 · Cited by 28 · see citing works

Detecting Deception in Online Social Networks

Alowibdi, J. S. (2015) · Figshare · Cited by 21 · see citing works

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Detecting deception in Online Social Networks

Alowibdi, J. S., Buy, U. A., Yu, P. S., & Stenneth, L. (2014) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2014.6921614 · Cited by 21 · see citing works

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 · see citing works

On-Demand Bot Detection and Archival System

Chavoshi, N., Hamooni, H., & Mueen, A. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3041021.3054733 · Cited by 20 · see citing works

Weblog Analysis for Predicting Correlations in Stock Price Evolutions

Kharratzadeh, M., & Coates, M. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v6i1.14323 · Cited by 17 · see citing works

Value and Misinformation in Collaborative Investing Platforms

Wang, T., Wang, G., Wang, B., Sambasivan, D., Zhang, Z., Li, X., Zheng, H.-T., & Zhao, B. Y. (2017) · ACM Transactions on the Web · doi:10.1145/3027487 · Cited by 17 · see citing works

Ranking Financial Tweets

Ceccarelli, D., Nidito, F., & Osborne, M. (2016) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2911451.2926727 · Cited by 12 · see citing works

CashTagNN: Using sentiment of tweets with CashTags to predict stock market prices

Rajesh, N., & Gandy, L. M. (2016) · International Conference on Intelligent Systems: Theories and Applications · doi:10.1109/sita.2016.7772262 · Cited by 11 · see citing works

TweetTrader.net: Leveraging Crowd Wisdom in a Stock Microblogging Forum

Sprenger, T. O. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14084 · Cited by 10 · see citing works

Enriching Digital Libraries with Crowdsensed Data

Cresci, S., Minutoli, S., Nizzoli, L., Tardelli, S., & Tesconi, M. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-11226-4_12 · Cited by 9 · see citing works

Echo Chambers in Investment Discussion Boards

Tang, S., Liu, Q., McQueen, M., Counts, S., Jain, A., Zheng, H.-T., & Zhao, B. Y. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14875 · Cited by 8 · see citing works

Echo Chambers in Investment Discussion BoardsPreprint

Tang, S., Liu, Q., McQueen, M., et al. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2943163 · Cited by 6 · see citing works

Nowcasting of Earthquake Consequences using Big Social Data

Cresci, S., Avvenuti, M., Polla, M. N. L., Meletti, C., & Tesconi, M. (2016) · IEEE Internet Computing · doi:10.1109/mic.2016.47 · Cited by 5 · see citing works

Kondenzer: Exploration and visualization of archived social media

Alonso, O., & Khandelwal, K. (2014) · Proceedings - International Conference on Data Engineering · doi:10.1109/icde.2014.6816741 · Cited by 4 · see citing works

Bot-hunters eye mischief in German election

Kupferschmidt, K. (2017) · Science · doi:10.1126/science.357.6356.1081 · Cited by 4 · see citing works

A decade of social bot detection

Cresci, S. (2020) · Communications of the ACM · doi:10.1145/3409116 · Cited by 323 · see citing works

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(2020) · Sabanci University · Cited by 197 · see citing works

Detection of Novel Social Bots by Ensembles of Specialized Classifiers

Sayyadiharikandeh, M., Varol, O., Yang, K.-C., Flammini, A., & Menczer, F. (2020) · Proceedings of the 29th ACM International Conference on Information & Knowledge Management · doi:10.1145/3340531.3412698 · Cited by 159 · see citing works

Botometer 101: social bot practicum for computational social scientists

Yang, K.-C., Ferrara, E., & Menczer, F. (2022) · Journal of Computational Social Science · doi:10.1007/s42001-022-00177-5 · Cited by 159 · see citing works

Charting the Landscape of Online Cryptocurrency Manipulation

Nizzoli, L., Tardelli, S., Avvenuti, M., Cresci, S., Tesconi, M., & Ferrara, E. (2020) · IEEE Access · doi:10.1109/access.2020.3003370 · Cited by 132 · see citing works

Over a decade of social opinion mining: a systematic review

Cortis, K., & Davis, B. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10030-2 · Cited by 137 · see citing works

Rise of the Machines? Examining the Influence of Social Bots on a Political Discussion Network

Hagen, L., Neely, S. R., Keller, T. E., Scharf, R., & Vasquez, F. E. (2020) · Social Science Computer Review · doi:10.1177/0894439320908190 · Cited by 102 · see citing works

Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines

Uyheng, J., & Carley, K. M. (2020) · Journal of Computational Social Science · doi:10.1007/s42001-020-00087-4 · Cited by 96 · see citing works

Fake news outbreak 2021: Can we stop the viral spread?

Khan, T., Michalas, A., & Akhunzada, A. (2021) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2021.103112 · Cited by 91 · see citing works

Identifying and Analyzing Cryptocurrency Manipulations in Social Media

Mirtaheri, M., Abu-El-Haija, S., Morstatter, F., Steeg, G. V., & Galstyan, A. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3059286 · Cited by 87 · see citing works

Crime and its fear in social media

Prieto-Curiel, R., Cresci, S., Muntean, C. I., & Bishop, S. R. (2020) · Humanities and Social Sciences Communications · doi:10.1057/s41599-020-0430-7 · Cited by 69 · see citing works

Social Bots Detection via Fusing BERT and Graph Convolutional Networks

Guo, Q., Xie, H., Li, Y., Ma, W., & Zhang, C. (2021) · Symmetry · doi:10.3390/sym14010030 · Cited by 65 · see citing works

Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic

Weng, Z., & Lin, A. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192416376 · Cited by 44 · see citing works

Emergent properties, models, and laws of behavioral similarities within groups of twitter users

Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2020) · Computer Communications · doi:10.1016/j.comcom.2019.10.019 · Cited by 40 · see citing works

TurkishBERTweet: Fast and reliable large language model for social media analysis

Najafi, A., & Varol, O. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124737 · Cited by 48 · see citing works

Investigating the difference between trolls, social bots, and humans on Twitter

Mazza, M., Avvenuti, M., Cresci, S., & Tesconi, M. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.09.022 · Cited by 36 · see citing works

Bots in Social and Interaction Networks

Mendoza, M., Tesconi, M., & Cresci, S. (2020) · ACM Transactions on Information Systems · doi:10.1145/3419369 · Cited by 36 · see citing works

A Survey on the Use of Graph Convolutional Networks for Combating Fake News

Varlamis, I., Michail, D., Glykou, F., & Tsantilas, P. (2022) · Future Internet · doi:10.3390/fi14030070 · Cited by 35 · see citing works

Twitter and Market Efficiency in Energy Markets: Evidence Using Lda Clustered Topic Extraction

Polyzos, E., & Wang, F. (2022) · Energy Economics · doi:10.1016/j.eneco.2022.106264 · Cited by 35 · see citing works

Bot stamina: examining the influence and staying power of bots in online social networks

Schuchard, R., Crooks, A. T., Stefanidis, A., & Croitoru, A. (2019) · Applied Network Science · doi:10.1007/s41109-019-0164-x · Cited by 36 · see citing works

Detection of fake news campaigns using graph convolutional networks

Michail, D., Kanakaris, N., & Varlamis, I. (2022) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2022.100104 · Cited by 34 · see citing works

Detecting inorganic financial campaigns on Twitter

Tardelli, S., Avvenuti, M., Tesconi, M., & Cresci, S. (2021) · Information Systems · doi:10.1016/j.is.2021.101769 · Cited by 31 · see citing works

Assessing dynamic qualities of investor sentiments for stock recommendation

Chang, J., Tu, W., Yu, C., & Qin, C. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102452 · Cited by 29 · see citing works

Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem

Assenmacher, D., Weber, D., Preuß, M., Valdez, A. S. C., Bradshaw, A., Roß, B., Cresci, S., Trautmann, H., Neumann, F., & Grimme, C. (2021) · Social Science Computer Review · doi:10.1177/08944393211012268 · Cited by 27 · see citing works

A Hybrid Deep Learning Architecture for Social Media Bots Detection Based on BiGRU-LSTM and GloVe Word Embedding

Ellaky, Z., Benabbou, F., Matrane, Y., & Qaqa, S. (2024) · IEEE Access · doi:10.1109/access.2024.3430859 · Cited by 26 · see citing works

Deep learning applications on cybersecurity: A practical approach

Miranda-García, A., Rego, A. Z., Pastor-López, I., Sanz, B., Tellaeche, A., de la Puerta, J. G., & Bringas, P. G. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126904 · Cited by 26 · see citing works

SEGCN: a subgraph encoding based graph convolutional network model for social bot detection

Liu, F., Li, Z., Yang, C., Gong, D., Lu, H., & Liu, F. (2024) · Scientific Reports · doi:10.1038/s41598-024-54809-z · Cited by 25 · see citing works

Fine-Tuned Understanding: Enhancing Social Bot Detection With Transformer-Based Classification

Sallah, A., Alaoui, E. A. A., Agoujil, S., Wani, M. A., Hammad, M. A., Maleh, Y., & El‐Latif, A. A. A. (2024) · IEEE Access · doi:10.1109/access.2024.3440657 · Cited by 27 · see citing works

On the efficacy of old features for the detection of new bots

Nicola, R. D., Petrocchi, M., & Pratelli, M. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102685 · Cited by 24 · see citing works

Developing Hybrid Deep Learning Models for Stock Price Prediction Using Enhanced Twitter Sentiment Score and Technical Indicators

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A language framework for modeling social media account behavior

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Demystifying Misconceptions in Social Bots Research

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Evaluating the Influence of Twitter Bots via Agent-Based Social Simulation

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FollowAKOInvestor: Stock recommendation by hearing voices from all kinds of investors with machine learning

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SEBD: A Stream Evolving Bot Detection Framework with Application of PAC Learning Approach to Maintain Accuracy and Confidence Levels

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Performance Comparison of LSTM and XGBOOST for Ether Price Prediction from Spam Filtered Tweets

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How Many Features Do We Need to Identify Bots on Twitter?

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New Dimensions of Information Warfare: The Economic Pillar—Fintech and Cryptocurrencies

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Unmasking social bots: how confident are we?

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Learning from Machines? Social Bots Influence on COVID-19 Vaccination-Related Discussions: 2021 in Review

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Social Bots Spreading Disinformation About Finance: Research Trends, and Ethical Challenges

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Adversarial botometer: adversarial analysis for social bot detection

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Tuning machine learning models to detect bots on Twitter

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SaraBotTagger - A Light Tool to Identify Bots in Twitter

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FollowAKOInvestor: Using Machine Learning to Hear Voices from All Kinds of Investors

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Assessment of Malicious Tweets Impact on Stock Market Prices

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Towards an Adversary-Aware ML-Based Detector of Spam on Twitter Hashtags

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Identifying Bots on Social Media through Coordinated Group Perception

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Conspiracy theories as instruments of power: The case of conspiracy beliefs in the wake of the 2023 earthquakes in Türkiye

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Big Data and Graph Deep Learning for Financial Decision Support from Social Networks: A Critical Review

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For Whom the Tale’s Told: Towards a Multidimensional Model of Targeted Narrative Persuasion in Information Operations

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Convolution Neural Network Based Model for Classification and Identification of Fake Profile on Social Network

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Analyzing the Efficacy of Bot Detection Methods on Twitter/XPreprint

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A Social Bot Recognition Method Combing Emojis InformationPreprint

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Social Media as an Agent of Influence: Twitter Bots in Russia - Ukraine War

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Identifying Unconvincing User on Social Media with Limited Features

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GMAE2: Stacking Graph Masked Autoencoder on Feature Autoencoder for Social Bot Detection

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Detection of LLM-powered bots using image classification

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A Process for Building Datasets that Enable the Application of Different Methods for Detecting Fake News and Social Bots

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The Influence of Social Media Bots on Financial MarketsPreprint

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Predicting Suspicious Arabic X Accounts Using Stylometric Features

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Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on TwitterPreprint

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$FAKE: Evidence of Spam and Bot Activity in Stock Microblogs on Twitter

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Assessment of Malicious Tweets Impact on Stock Market Prices

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Characterizing Social Bots Spreading Financial Disinformation

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More than just Noise? Examining the Information Content of Stock Microblogs on Financial Markets

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