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Published:
A personal collection of computational methods books (online only):
Published:
A collection of commonly used computational tools:
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:
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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(Normative) theorists have argued that conversation is the soul of democracy. However, political discussions might not be as ideal as the theorists envisioned. This talk summarizes a series of computational studies using large-scale social media data to examine the structures and consequences of political discussions online. Specifically, this talk discusses both the positive and negative sides: the organizational principles of web forum discussions and how they could help produce common ground among the users, how diffusion structures are related to cross-ideological interaction on Twitter, to what extent movement-related discussions on LIHKG are leaderless, and finally how to identify the causal effects of incivility such as swearing and name-calling using machine learning and text mining.
Undergraduate course, School of Journalism and Communication, CUHK, 2020
COMM3650 (2020-2021 Term 1)
Teacher: Prof. LIANG Hai (NAH310)
NAH209: Th 2:30 PM - 5:15 PM
Master course, School of Journalism and Communication, CUHK, 2021
COMM5631 (2021-2022 Term 1)
Teacher: Prof. LIANG Hai (NAH310)
NAH209: Th 6:30 PM - 9:15 PM
PhD course, School of Journalism and Communication, CUHK, 2022
COMM6320 (2021-2022 Term 2)
Teacher: Prof. LIANG Hai (NAH310)
NAH209: We 2:30 PM - 5:15 PM