LINGSHU HU is a Ph.D. candidate at the Missouri School of Journalism. His research focuses on computational methods, digital media, media analytics, and media psychology. He applies various computational methods, such as machine learning and deep learning, to analyze opinion expression patterns and self-presentation on computer-mediated environments. He also conducts experiments and surveys to further explore the implications of the computational findings.
Lingshu Hu has collaborated with scholars and applied computational methods in health communication, media sociology, political communication, and social psychology. He is also an R and Python programmer and develops software to facilitate communication research.
Besides research, Lingshu Hu is a graduate instructor for J1200 “Fundamentals of Visual Journalism and Strategic Communication” and has also been a co-instructor for J2150 “Fundamentals of Multimedia Journalism.”
Lingshu Hu holds a master’s degree in Gender, Media, and Culture from the London School of Economics and Political Science (LSE). Prior to pursuing his doctoral degree, he worked as a news editor and data journalist for four years at the Guangzhou Daily, China.
- Hu, L., and Kearney, M.W. (Accepted). Gendered Tweets: Computational text analysis of gender differences in political discussions on Twitter. Journal of Language and Social Psychology.
- Frisby, C.M., and Hu, L. (2019). A light that walks forward: Native Americans and expressions of micro aggressions on social media. International Review of Social Sciences, 7(7), 332-346.
- Hu, L. (2018) Is masculinity ‘deteriorating’ in China? Changes of masculinity representation in Chinese film posters from 1951 to 2016. Journal of Gender Studies, 27(3), 335-346.
- Hu, L. (2016) Communication effects of data journalism on Weibo (microblog). Editorial Friend (2), 70-74. (In Chinese)
- He, L., Hu, L., Li, W., and Zhang, Z. (2016). Title attractors and negative energy: Language style analysis of organizational media’s official accounts on WeChat. The Press (13), 42-47. (In Chinese)
- He, L., Hu, L., and Yu, L. (2016). The communication features and the psychological mechanism of rumors on WeChat. In X. Tang, X. Wu, and C. Huang (Eds.), Annual Report on Development of New Media in China (No.7) (pp. 102-118). Social Science Academic Press. (In Chinese)
- Zhang, W., Hu, L., and Park, J. (2020). When Virus Goes Political: A Computerized Text Analysis of Crisis Attribution on Covid-19 Pandemic. Paper presented at the 103rd AEJMC Annual Conference, Virtual.
- Hu, L., and Frisby, C.M. (2020). Reconstruct the “Spiral”: Positive and Negative Motivations Predicting Outspokenness in Online and Offline Scenarios. Paper presented at the 70th ICA Annual Conference, Gold Coast, Australia (Virtual).
- Hu, L., and Kearney, M.W. (2020). Gendered Tweets: Text Analysis of Gender Differences in Political Discussions on Twitter. Paper presented at the 70th ICA Annual Conference, Gold Coast, Australia (Virtual).
- Hu, S., and Hu, L. (2020). Predictors of Perceived Information Credibility in an Online Health Forum: A Computerized Content Analysis of Big Dataset. Paper presented at the 70th ICA Annual Conference, Gold Coast, Australia (Virtual).
- Kearney, M.W., Hu, L., and Alieva, I. (2019). Classifying Twitter Bots. Paper presented at the 102nd AEJMC Annual Conference, Toronto, Canada.
- Hu, L., and Frisby, C.M. (2019). Outside the “Spiral”: Factors Predicting Outspokenness in Online and Offline scenarios. Paper presented at the 69th ICA Annual Conference, Washington, D.C.
- Abeyta, A., Alieva, I., Park, J., Warner, B.R., Hu, L., and Kearney, M.W. (2019). Analyzing Tweets from Candidates for the U.S. Congress in the 2018 U.S. Midterm Elections. Paper presented at the 88th Annual Meeting of Central States Communication Association, Omaha, NE.
- Hu, L., and Kearney, M.W. (2018). Speaking in a woman’s name: Gender difference of political expressive participation on Twitter. Paper presented at the 101st AEJMC Annual Conference, Washington, D.C.
- Hu, L., and Kearney, M.W. (2019). healthforum: Scrape Patient Forum data (Version 0.0.1) [R package]. CRAN. https://CRAN.R-project.org/package=healthforum
- Kearney, M.W., Hu, L., and Alieva, I. (2019). ppcong: Interfacing with ProPublica’s ‘Congress’ API (Version 0.0.2) [R package]. CRAN. https://CRAN.R-project.org/package=ppcong
- Kearney, M.W., Hvitfeldt, E., and Hu, L. (2019). textfeatures: Extracts features from text (Version 0.3.3) [R package]. CRAN. https://CRAN.R-project.org/package=textfeatures
Updated: October 6, 2020