- Enhanced Morality Lexicon - This lexicon is the expanded and enhanced version of the Moral Foundations Dictionary, which was originally created by Graham and colleagues (Graham et al., 2013). We syntactically disambiguated the original dictionary and increased it in size and scope. We manually validated our version and evaluated it against benchmarks. Our Enhanced Morality Lexicon (EML) contains a list of 4,636 morality related words. Related paper from WASSA Workshop @ NAACL: Rezapour R., Shah S., & Diesner J. (2019) Enhancing the measurement of social effects by capturing morality.
- Rezapour, R., Shah, S., & Diesner, J. (2019). Enhancing the measurement of social effects by capturing morality. Proceedings of the 10th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA). Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Minneapolis, MN.
- Rezapour, Rezvaneh; Diesner, Jana (2019): Expanded Morality Lexicon. University of Illinois at Urbana-Champaign. doi: 10.13012/B2IDB-3805242_V1.1
- WikiCSSH - Extracting Computer Science Subject Headings from Wikipedia. Related paper from the Scientific Knowledge Graphs Workshop co-located with the Conference on Theory and Practice of Digital Libraries (TPDL): Han, Kanyao; Yang, Pingjing; Mishra, Shubhanshu; Diesner, Jana. 2020. WikiCSSH: Extracting Computer Science Subject Headings from Wikipedia.
- Project Website: https://uiuc-ischool-scanr.github.io/WikiCSSH/
- Code: https://github.com/uiuc-ischool-scanr/WikiCSSH
- Data: https://databank.illinois.edu/datasets/IDB-0424970
- Han, K., Yang, P., Mishra, S., & Diesner, J. (2020). WikiCSSH: Extracting Computer Science Subject Headings from Wikipedia. In Proceedings of Scientific Knowledge Graphs Workshop co-located with 24th International Conference on Theory and Practice of Digital Libraries (TPDL), held online.
- Han, Kanyao; Yang, Pingjing; Mishra, Shubhanshu; Diesner, Jana (2020): WikiCSSH - Computer Science Subject Headings from Wikipedia. University of Illinois at Urbana-Champaign. doi: 10.13012/B2IDB-0424970_V1
- ConText -- Connections and Texts. ConText supports a) the construction of network data from natural language text data, a process also known as relation extraction and b) the joint analysis of text data and network data.
- Diesner, J. (2014). ConText: Software for the Integrated Analysis of Text Data and Network Data. Paper presented at the Social and Semantic Networks in Communication Research. Preconference at Conference of International Communication Association (ICA), Seattle, WA.
- Diesner, J., Aleyasen, A., Chin, C., Mishra, S., Soltani, K., Tao, L., Jiang, M., Korrapati, H., Parulian, N., & Jiang, L. (2019). ConText: Network Construction from Texts [Software]. Available from http://context.ischool.illinois.edu/
- Adversarial Network Simulation and Analysis Framework -- This project has resulted in an open-source framework that allows researchers to assess the robustness of network structures and metrics after adversarial attacks. Our approach also allows the exploration of mitigation strategies.
- Parulian, N. N., Lu, T., Mishra, S., Avram, M., & Diesner, J. (2020). Effectiveness of the Execution and Prevention of Metric-Based Adversarial Attacks on Social Network Data. Information, 11(6), 306. doi: 10.3390/info11060306
- o Avram, M. V., Mishra, S., Parulian, N. N., & Diesner, J. (2019, August). Adversarial perturbations to manipulate the perception of power and influence in networks. In 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 986-994). IEEE. doi: 10.1145/3341161.3345026
- SAIL -- Sentiment Analysis and Incremental Learning tool. SAIL is a tool which gives users the convenience of doing sentiment analysis using pre-trained models. The tool also supports incremental learning of the existing models by adding new labeled data.
- Mishra, S., Diesner, J., Byrne, J., & Surbeck, E. (2015). Sentiment Analysis with Incremental Human-in-the-Loop Learning and Lexical Resource Customization. In Proceedings of the 26th ACM Conference on Hypertext & Social Media, pp. 323-325. ACM. http://dl.acm.org/citation.cfm?id=2791022
- Diesner, J., Mishra, S., Tao, L., & Chin, C. (2015). SAIL: Sentiment Analysis and Incremental Learning [Software]. Available from https://github.com/uiuc-ischool-scanr/SAIL