Published in Social Science Computer Review, 2014
Recommended citation: Liang, H. (2014). Coevolution of political discussion and common ground in web discussion forum. Social Science Computer Review, 32(2), 155-169. doi: 10.1177/0894439313506844
Published in Human Communication Research, 2014
Recommended citation: Liang, H. (2014). The organizational principles of online political discussion: A relational event stream model for analysis of web forum deliberation. Human Communication Research, 40(4), 483-507. doi: 10.1111/hcre.12034
Published in PLoS ONE, 2015
Recommended citation: Liang, H., & Fu, K. W. (2015). Testing propositions derived from Twitter studies: Generalization and replication in computational social science. PLoS ONE, 10(8), e0134270. doi: 10.1371/journal.pone.0134270
Published in Journal of Computer-Mediated Communication, 2017
Recommended citation: Liang, H., & Fu, K. W. (2017). Information similarity, overload, and redundancy: Unsubscribing information sources on Twitter. Journal of Computer-Mediated Communication, 22(1), 1–17. doi: 10.1111/jcc4.12178
Published in New Media & Society, 2017
Recommended citation: Liang, H., Shen, F., & Fu, K. W. (2017). Privacy protection and self-disclosure across societies: A study of global Twitter users. New Media & Society, 19(9), 1476-1497. doi: 10.1177/1461444816642210
Published in Journal of Communication, 2018
Recommended citation: Liang, H. (2018). Broadcast versus viral spreading: The structure of diffusion cascades and selective sharing on social media. Journal of Communication, 68(3), 525–546. doi: 10.1093/joc/jqy006
Published in Communication Research, 2019
Recommended citation: Liang, H., & Fu, K. W. (2019). Network redundancy and information diffusion: The impacts of information redundancy, similarity, and tie strength. Communication Research, 46(2), 250-272. doi: 10.1177/0093650216682900
Published in Journal of Communication, 2021
Recommended citation: Liang, H., & Zhang, X. (2021). Partisan bias of perceived incivility and its political consequences: Evidence from survey experiments in Hong Kong. Journal of Communication, 71(3), 357–379. doi: 10.1093/joc/jqab008
Published in Human Communication Research, 2023
Recommended citation: Liang, H. & Ng, Y. M. M. (2023). The expression effects of uncivil disagreement: The mechanisms of cognitive dissonance and self-perception. Human Communication Research, 49(3), 251–259. doi: 10.1093/hcr/hqac032
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This talk summarized the popular empirical models including user-centric models, cascade-centric models, and a newly developed model by combining the two.
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This is a tutorial for the PhD students in the school of journalism and communication at CUHK.
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This talk summarizes a series of computational studies using large-scale social media data to examine the structures and consequences of political discussions online.
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Using political incivility as an example, this talk presents a dictionary-based classification method, by leveraging word embeddings, could outperform supervised machine learning methods to detect uncivil content in online comments. Furthermore, this talk shows that machine learning models could help identify the causal effects of political incivility on engagement and contagions, by controlling for textual confounders.
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NCCU
Undergraduate course, School of Journalism and Communication, CUHK, 2022
COMM3650 (2022-2023 Term 1)
Teacher: Prof. LIANG Hai (NAH310)
NAH209: We 10:30 AM - 1:15 PM
Master course, School of Journalism and Communication, CUHK, 2022
COMM5631 (2022-2023 Term 2)
Teacher: Prof. LIANG Hai (NAH310)
NAH209: Th 6:30 PM - 9:15 PM
PhD course, School of Journalism and Communication, CUHK, 2022
COMM6320 (2022-2023 Term 2)
Teacher: Prof. LIANG Hai (NAH310)
NAH209: We 2:30 PM - 5:15 PM