Research Project Overview Montclair State University
Faculty Advisor: Christopher Leberknight
Student: Irene McGinniss
Tentative Weekly Meeting: TBD
Virtual Meeting Links
Weekly Log: Complete the log each week to record your progress on the project. At the end of the semester you will submit all logs to your faculty advisor. The log can be found here
Project Title: Information Manipulation on Social Media: Bots, Misinformation and Censorship
Project Summary The aim of this project is to investigate methods to detect bots (software programs) and misinformation. Information control is a major threat to cybersecurity that can destabilize our democracy. The information we consume has a direct impact on how we function as a society, what we believe, who we connect with, and how we make decisions. By participating in this project you will learn how graph-based methods and machine learning can be used to detect bots and misinformation in social media. You will experiment with online fact checking and bot detection tools and learn how to identify bots from existing datasets. The dependency on online social networks (OSN's) and their ability to rapidly spread information provides an opportunity for social bots to control and manipulate the information we read. How much information do you read online is created by bots? How do you know if the information you read online is "fake news?" What algorithms or techniques can help us identify bots? These are some questions we will investigate in this project. Social media platforms can produce echo-chambers, which lead to polarization and can encourage the spread of false information .
 Kumar, S., & Shah, N. (2018). False information on web and social media: A survey. arXiv preprint arXiv:1804.08559.
Project Objectives: The aim of the project is to design a network-based model for detecting misinformation in social media.
- A technical report that describes the methodology and evaluation results. (Mandatory)
- A technical paper submitted to a research conference.
- Well-documented code repository
Some projects to explore
||Network Science Tutorial
||Machine Learning Tutorial
||Misinformation and Censorship on Social Media Literature Review
||Analysis & Results
Week 1: Network Science
- Tutorial on Statistical Analysis of Networks>
Week 2: Machine Learning
- https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_tutorial.pdf (Start with Chapters 1-6)
Week 3: Misinformation and Censorship on Social Media
- Kumar, S., & Shah, N. (2018). False information on web and social media: A survey. arXiv preprint arXiv:1804.08559.
Network Science Research Papers by Filippo Menczer (https://cnets.indiana.edu/fil/papers/)
- 4 Reasons Why Social Media Make Us Vulnerable to Manipulation. Menczer, F. In Proc. 14th ACM Conference on Recommender Systems (RecSys), 2020. View the video for this talk here: https://www.youtube.com/watch?v=0jGudEM31ds
- Detection of Novel Social Bots in Twitter. Sayyadiharikandeh, M.; Varol, O.; Yang, K.; Flammini, A.; and Menczer, F. 2020. Poster Presentation at International Conference on Computational Social Science (IC2S2); Poster Presentation at International Conference on Network Science (NetSci)
- Detection of Novel Social Bots in Twitter (Supplemental Material) Sayyadiharikandeh, M.; Varol, O.; Yang, K.; Flammini, A.; and Menczer, F. 2020. Poster Presentation at International Conference on Computational Social Science (IC2S2); Poster Presentation at International Conference on Network Science (NetSci)
- Tackling misinformation: What researchers could do with social media data. Pasquetto, I. V.; Swire-Thompson, B.; and others HKS Misinformation Review, 1(8). 2020.
- Fakey: A Game Intervention to Improve News Literacy on Social Media. Micallef, N.; Avram, M.; Menczer, F.; and Patil, S. Proc. ACM Human-Computer Interaction, 5(CSCW1): 6. 2021. In press. Presented at CSCW 2021
- BotSlayer: DIY Real-Time Influence Campaign Detection. Hui, P.; Yang, K.; Torres-Lugo, C.; and Menczer, F. In Proc. 14th Intl. AAAI Conf. on Web and Social Media (ICWSM), pages 980–982, 2020.
- The Hoaxy Misinformation and Fact-Checking Diffusion Network. Hui, P.; Shao, C.; Flammini, A.; Menczer, F.; and Ciampaglia, G. L. In Proc. 12TH Intl. AAAI Conf. on Web and Social Media (ICWSM), pages 528–530, 2018.
- Feature Engineering for Social Bot Detection Varol, O.; Davis, C. A.; Menczer, F.; and Flammini, A. In Dong, G.; and Liu, H., editor(s), Feature Engineering for Machine Learning and Data Analytics, of Data Mining and Knowledge Discovery Series, 12, pages 311–334. Chapman and Hall/CRC Press, 2018.
Other Papers Network Science
- Bovet, A., & Makse, H. A. (2019). Influence of fake news in Twitter during the 2016 US presidential election. Nature communications, 10(1), 1-14.
- Shao, C., Ciampaglia, G. L., Varol, O., Yang, K. C., Flammini, A., & Menczer, F. (2018). The spread of low-credibility content by social bots. Nature communications, 9(1), 1-9.
- Chowdhury, S., Khanzadeh, M., Akula, R., Zhang, F., Zhang, S., Medal, H., ... & Bian, L. (2017). Botnet detection using graph-based feature clustering. Journal of Big Data, 4(1), 1-23.
- Min, Y., Jiang, T., Jin, C., Li, Q., & Jin, X. (2019). Endogenetic structure of filter bubble in social networks. Royal Society open science, 6(11), 190868.
- Wagner, C., Mitter, S., Körner, C., & Strohmaier, M. (2012, April). When Social Bots Attack: Modeling Susceptibility of Users in Online Social Networks. In # MSM (pp. 41-48).
- Nguyen, M., Aktas, M., & Akbas, E. (2020). Bot Detection on Social Networks Using Persistent Homology. Mathematical and Computational Applications, 25(3), 58.
- Yang, K. C., Varol, O., Davis, C. A., Ferrara, E., Flammini, A., & Menczer, F. (2019). Arming the public with artificial intelligence to counter social bots. Human Behavior and Emerging Technologies, 1(1), 48-61.
- Salge, C. A. D. L., & Karahanna, E. (2018). Protesting corruption on Twitter: Is it a bot or is it a person? Academy of Management Discoveries, 4(1), 32-49.
- Wald, R., Khoshgoftaar, T. M., Napolitano, A., & Sumner, C. (2013, August). Predicting susceptibility to social bots on twitter. In 2013 IEEE 14th International Conference on Information Reuse & Integration (IRI) (pp. 6-13). IEEE.
- Schuchard, R., Crooks, A., Stefanidis, A., & Croitoru, A. (2018, December). Bots in nets: Empirical comparative analysis of bot evidence in social networks. In International Conference on Complex Networks and their Applications (pp. 424-436). Springer, Cham.
- Lou, X., Flammini, A., & Menczer, F. (2019). Information pollution by social bots. arXiv preprint arXiv:1907.06130.
- Abou Daya, A., Salahuddin, M. A., Limam, N., & Boutaba, R. (2019, April). A graph-based machine learning approach for bot detection. In 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM) (pp. 144-152). IEEE.
- Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., & Lazer, D. (2019). Fake news on Twitter during the 2016 US presidential election. Science, 363(6425), 374-378.
- Shi, P., Zhang, Z., & Choo, K. K. R. (2019). Detecting malicious social bots based on clickstream sequences. IEEE Access, 7, 28855-28862.
- Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016). The rise of social bots. Communications of the ACM, 59(7), 96-104.
- Jiang, J., Ren, X., & Ferrara, E. (2021). Social media polarization and echo chambers: A case study of COVID-19. arXiv preprint arXiv:2103.10979.
- Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146-1151.