Research Papers


    Verschuuren, P. J., Gao. J., Van Eeden, A., Oikonomou, S., & Bandhakavi, A. (2023). Logically at Factify 2: A multi-modal fact checking system based on evidence retrieval techniques and transformer encoder architecture. In AAAI’23: De-Factify 2: 2nd Workshop on Multimodal Fact Checking and Hate Speech Detection, Washington, DC, USA. ("Best Paper" award & ranked 3rd in leaderboard) Access via pdf [slides]

    Gao. J., Hoffmann, H. F., Oikonomou, S., Kiskovski, D., & Bandhakavi, A. (2022). “Logically at Factify 2022: Multimodal Fact Verification.”, In: AAAI'22: First Workshop on Multimodal Fact-Checking and Hate Speech Detection, Vancouver, BC, Canada. (Ranked 1st in leaderboard) Access via arXiv preprint arXiv:2112.09253. [slides]

    Gao. J., Han S., Song X., Ciravegna, F. (2020). “RP-DNN: A Tweet level propagation context based deep neural networks for early rumor detection in Social Media”, In: The LREC 2020 Proceedings. The International Conference on Language Resources and Evaluation, 11-16 May 2020, Marseille. LREC 2020 (accepted for an Oral presentation). [preprint], [Code & models]

    Han S., Gao, J., Ciravegna, F. (2019). "Neural Language Model Based Training Data Augmentation for Weakly Supervised Early Rumor Detection", The 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019), Vancouver, Canada, 27-30 August, 2019 [Acceptance Rate: 14% among 41 out of total 286 submissions] [download]

    Han S., Gao, J., Ciravegna, F. (2019). "Data Augmentation for Rumor Detection Using Context-Sensitive Neural Language Model With Large-Scale Credibility Corpus", Seventh International Conference on Learning Representations (ICLR) LLD,New Orleans, Louisiana, US (OpenReview link) [spotlight] [Baseline Sourcecode] [Fine-tuned ELMo model] [Dataset]

    Ciravegna, F., Gao, J., Ireson, N., Copeland, R., Walsh, J., & Lanfranchi, V. (2019, May). "Active 10: Brisk Walking to Support Regular Physical Activity." In Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare (pp. 11-20). ACM.

    Ciravegna, F. Gao, J., Ingram, C., Ireson, N., Lanfranchi, V., & Humasak S. (2018). "Mapping Mobility to Support Crisis Management." 15th Proceeding of International Conference on Information Systems for Crisis Response and Management (ISCRAM).. ISCRAM Association. 2018.

    Zhang, Z., Gao, J., & Ciravegna, F. (2018). SemRe-Rank: Incorporating Semantic Relatedness to Improve Automatic Term Extraction Using Personalized PageRank. ACM Transactions on Knowledge Discovery from Data (TKDD). [download] [[Code]]

    Yuan, Y., Gao, J., & Zhang, Y. (2017, October). Supervised Learning for Robust Term Extraction. In The proceedings of 2017 International Conference on Asian Language Processing (IALP). IEEE.

    Zhang, Z., Gao, J., & Ciravegna, F. (2016, February). JATE 2.0: Java Automatic Term Extraction with Apache Solr. In LREC. [Poster] [Code]

    Zhang, Z., Gao, J., & Gentile, A. L. (2015). The LODIE team (University of Sheffield) Participation at the TAC2015 Entity Discovery Task of the Cold Start KBP Track. In: Proceedings of the 2015 Text Analysis Conference. TAC Knowledge Base Population (KBP) 2015, 16-17 Nov 2015, Gaithersburg, Maryland USA. [download] [Poster]

    Gao, J., & Mazumdar, S. (2015, May). Exploiting linked open data to uncover entity types. In Semantic Web Evaluation Challenge (pp. 51-62). Springer, Cham. [Slides] [Code]