TAMING SOCIAL BOTS: DETECTION, EXPLORATION AND MEASUREMENT
ABSTRACT
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Social bots have been around for over a decade now. Social bots are capable of swaying political opinion, spreading false information, and recruiting for terrorist organizations. Social bots use various sophisticated techniques by adopting emotions, sympathy following, synchronous deletions, and profile molting.
There are several approaches proposed in the literature for detection, exploration, and measuring social bots. We will provide a comprehensive overview of the existing work from data mining and machine learning perspective, discuss relative strengths and weaknesses of various methods, make recommendations for researchers and practitioners, and propose novel directions for future research in taming the social bots. The tutorial will also discuss pitfalls in collecting and sharing data on social bots.
Presenter Bios
Nikan Chavoshi
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Dr. Nikan Chavoshi has joined Oracle as a Senior Member of Technical Staff in June 2018. Earlier, she earned her PhD degree in Computer Science from the University of New Mexico. Her research interest is in time series mining and temporal activity analysis. Her graduate work was on analyzing temporal behavior of automated accounts in Twitter, for which, she was awarded the Outstanding Graduate Student for the CS Department in 2018. In her PhD, she worked with Dr. Abdullah Mueen and designed a near real-time system, named DeBot, to detect automated accounts in Twitter. Dr. Chavoshi has published research articles in top web mining venues including WWW, ICDM, SocInfo, ASONAM and KAIS.
Amanda Minnich
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Dr. Amanda J. Minnich is a Machine Learning Research Scientist and Molecular Data-Driven Modeling Team Lead at Lawrence Livermore National Lab (LLNL). At LLNL she is part of the multi-institution ATOM Consortium, where she applies Machine Learning techniques to biological data for drug discovery purposes. Dr. Minnich received a BA in Integrative Biology from UC Berkeley (2009) and an MS (2014) and PhD with Distinction (2017) in Computer Science from the University of New Mexico. Her thesis topic was “Spam, Fraud, and Bots: Improving the Integrity of Online Social Media Data.” While at UNM she was named an NSF Graduate Research Fellow, a PiBBs Fellow, a Grace Hopper Scholar, and the Outstanding Graduate Student for the CS Department in 2017. She has published her work at and served on Program Committees for top conferences including WWW, ASONAM, KDD, ICDM, SC, GTC, and ICWE, and has been issued a patent for her dissertation work. Dr. Minnich also has a passion for advocating for women in tech; she co-founded and served as President of UNM’s first chartered Women in Computing group, she frequently volunteers at women in tech events, and she will be co-chairing the Artificial Intelligence track at Grace Hopper Celebration 2019.
Abdullah Mueen
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Abdullah Mueen is an Assistant Professor in Computer Science at University of New Mexico since 2013. Earlier he was a Scientist in the Cloud and Information Science Lab at Microsoft Corporation. His major interest is in temporal data mining with a focus on two unique types of signals: social networks and electrical sensors. He has been actively publishing in the data mining conferences including KDD, ICDM and SDM, and journals including DMKD and KAIS. He has received runner-up award in the Doctoral Dissertation Contest in KDD 2012. He has won the best paper award in SIGKDD 2012. His research is funded by NSF, DARPA and AFRL. Earlier, he earned PhD degree at the University of California at Riverside and BSc degree at Bangladesh University of Engineering and Technology.
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