RESEARCH

Peer Reviewed Publications:

Liu, R., Wei, J., Jia, C., & Vosoughi, S. (2021). Modulating language models with emotions. The Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021).

 

Liu, R.,  Jia, C., & Vosoughi, S. (2021). A transformer-based framework for flipping political polarity of news articles. Human Computer Interaction Journal (PACM HCI) Vol. 5 – Proceedings of the 23rd ACM Conference on Computer–Supported Cooperative Work and Social Computing (CSCW 2021).

[PDF]  |  [BibTeX]  |  [Google Scholar|  [around 26% acceptance rate]

Liu, R.,  Jia, C., Wei, J., Xu, G., Wang, Li., & Vosoughi, S. (2021). Mitigating political bias in language models through reinforced calibration. The Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021). *Best Paper Award* (3 out of 9071 submissions)

[PDF] [21% acceptance rate]

Liu, R., Wang, L., Jia, C., & Vosoughi, S. (2021). Political depolarization of news articles using attribute-aware word embeddings. The Proceedings of the 15th International AAAI Conference on Web and Social Media (ICWSM 2021).

[PDF]  |  [BibTeX]  |  [Google Scholar|  [around 20% acceptance rate]

Jia, C. (2020). Chinese automated news: A comparison between expectations and perceived quality. International Journal of Communication. 14(2020), 2611–2632.

[PDF]  |  [BibTeX]  |  [Google Scholar]

Jia, C., & Gwizdka, J. (2020). An eye-tracking study of differences in reading between automated news and human-written news. Davis, F., Riedl, R., Brocke, J., Léger, P., Randolph, A., Fischer, T. (Eds.), Information Systems and Neuroscience. vol 43, 100–110. Springer International Publishing. https://doi.org/10.1007/978-3-030-60073-0_12

[PDF]  |  [BibTeX]  |  [Google Scholar]

Liu, R., Xu, G., Jia, C., Ma, W., Wang, L., & Vosoughi, S. (2020). Data Boost: Text data augmentation through reinforcement learning guided conditional generation. The Proceedings of 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020). 90319041. Association for Computational Linguistics. http://dx.doi.org/10.18653/v1/2020.emnlp-main.726

[PDF]  |  [BibTeX]  |  [Google Scholar]  |  [22.4% acceptance rate]

Book Chapters 

Fang, K. & Jia, C. (2020). The global trend of journalism research in 2019: The year of deep journalism. An Annual Report on Chinese Journalism (2020). (in Chinese). Beijing: People’s Daily Press.

 

Fang, K. & Jia, C. (2019). The global trend of journalism research in 2018: New contexts, new methodologies, and the same questions (in Chinese). An Annual Report on Chinese Journalism (2019). Beijing: People’s Daily Press.

Fang, K. & Jia, C. (2018). The global trend of journalism research in 2017: Rapid changes and insistence (in Chinese). An Annual Report on Chinese Journalism (2018). Beijing: People’s Daily Press.

Jia, C. (2017). The analysis of the Financial Times’ social media strategy in China (in Chinese). Transition and Integration. Beijing: Posts and Telecom Press.

Jia, C. (2017). The growth of China Youth Daily (in Chinese). Transition and Integration. Beijing: Posts and Telecom Press.

Peer Reviewed Conference Papers (selected):

Jia, C., Johnson, T., Wallace, R., & Lee, T. (2021). News algorithm appreciation or aversion? Examining media trust and algorithm attitudes. The 71st Annual International Communication Association Conference (ICA 2021), Human-Machine Communication Interest Group, May 27–31, 2021, Virtual Conference.

Presenter: Chenyan Jia  |   [Conference Information]   |  Oral Presentation

Jia, C., & Liu, R. (2021). Examine relative hostile media effect with a transformer-based framework: A computational method to flip the polarity of news headline and body text. The 71st Annual International Communication Association Conference (ICA 2021), Communication & Technology Division, May 27–31, 2021, Virtual Conference.

Presenter: Chenyan Jia  |   [Conference Information]   |  Oral Presentation

Jia, C., and Johnson, T. (2020). Source credibility matters: Does automated journalism inspire selective exposure? The 70th Annual International Communication Association Conference(ICA 2020), Communication & Technology Division, May 21-25, 2020, Virtual Conference.

Presenter: Chenyan Jia  |   [Conference Information]   |  Oral Presentation

Jia, C., and Woolley, S. (2020). Social scaffolding or computational propaganda?: A comparative analysis of automated journalism in China and the United States. The 70th Annual International Communication Association Conference, Political Communication Division (ICA 2020). May 21-25, 2020, Virtual Conference.

Presenter: Chenyan Jia  |   [Conference Information]  |  Oral Presentation 

Jia, C. (2019). Chinese automated news: A comparison between prior expectations and actual perceptions. The Association for Education in Journalism and Mass Communication Summer’s Annual Conference (AEJMC 2019), The Communication Technology Division, August 7-10, Toronto, Canada.

Presenter: Chenyan Jia  |   [Conference Information]  |  Oral Presentation

Jia, C. (2019). Chinese automated news: Readability, expertise and credibility. The Association for Education in Journalism and Mass Communication (AEJMC) Mid-winter Conference, The Communication Technology Division, March 1-2, the University of Oklahoma, Norman, Oklahoma, the United States.

Presenter: Chenyan Jia  |  Oral Presentation

Jia, C., Yuan, Y., & Wang, Z. (2017). The readability analysis of Chinese and English automated news. International Communication Association (ICA) International New Media Forum, Panel 2: News Production in the Digital Age, November 11, Shanghai, China. (Won Top Paper Award.)

Presenter: Chenyan Jia  |  Oral Presentation​

Jia, C. (2017). The development mode of printed media' s WeChat accounts in Zhejiang province: Based on empirical research on Urban Express, Qianjiang Evening News, and Hangzhou Daily. International Symposium on Chinese Newspapers, Panel 2A, June 1 -2, Hong Kong, China.

Presenter: Chenyan Jia  |  Oral Presentation​

Co-author(s): Ruibo Liu