Hai Liang

1.6k total citations
47 papers, 1.0k citations indexed

About

Hai Liang is a scholar working on Sociology and Political Science, Communication and Statistical and Nonlinear Physics. According to data from OpenAlex, Hai Liang has authored 47 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Sociology and Political Science, 26 papers in Communication and 13 papers in Statistical and Nonlinear Physics. Recurrent topics in Hai Liang's work include Social Media and Politics (25 papers), Misinformation and Its Impacts (10 papers) and Opinion Dynamics and Social Influence (10 papers). Hai Liang is often cited by papers focused on Social Media and Politics (25 papers), Misinformation and Its Impacts (10 papers) and Opinion Dynamics and Social Influence (10 papers). Hai Liang collaborates with scholars based in Hong Kong, United States and China. Hai Liang's co-authors include King‐Wa Fu, Isaac Chun‐Hai Fung, Zion Tsz Ho Tse, Francis Lee, Fei Shen, Gary Tang, Edmund W. Cheng, Samson Yuen, Sierra Hovet and Patrick Ip and has published in prestigious journals such as PLoS ONE, Journal of Business Research and Computers in Human Behavior.

In The Last Decade

Hai Liang

42 papers receiving 988 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Hai Liang Hong Kong 18 710 323 246 178 137 47 1.0k
Nicholas Proferes United States 12 604 0.9× 303 0.9× 258 1.0× 80 0.4× 40 0.3× 26 1.2k
Nicolás Velásquez United States 10 565 0.8× 147 0.5× 245 1.0× 329 1.8× 47 0.3× 15 891
Marcus Messner United States 14 632 0.9× 500 1.5× 139 0.6× 227 1.3× 50 0.4× 28 970
Yunhao Zhang United States 5 1.0k 1.4× 360 1.1× 280 1.1× 237 1.3× 52 0.4× 7 1.3k
Joseph Downing United Kingdom 9 510 0.7× 159 0.5× 139 0.6× 148 0.8× 35 0.3× 20 650
Timothy Graham Australia 12 350 0.5× 153 0.5× 129 0.5× 146 0.8× 44 0.3× 61 648
Saifuddin Ahmed Singapore 20 901 1.3× 639 2.0× 331 1.3× 86 0.5× 21 0.2× 81 1.5k
Chen Min China 11 395 0.6× 256 0.8× 100 0.4× 69 0.4× 35 0.3× 33 848
Jacob Groshek United States 18 581 0.8× 624 1.9× 144 0.6× 119 0.7× 24 0.2× 63 1.1k
Cameron Martel United States 13 764 1.1× 290 0.9× 215 0.9× 143 0.8× 15 0.1× 22 1.0k

Countries citing papers authored by Hai Liang

Since Specialization
Citations

This map shows the geographic impact of Hai Liang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hai Liang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hai Liang more than expected).

Fields of papers citing papers by Hai Liang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hai Liang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hai Liang. The network helps show where Hai Liang may publish in the future.

Co-authorship network of co-authors of Hai Liang

This figure shows the co-authorship network connecting the top 25 collaborators of Hai Liang. A scholar is included among the top collaborators of Hai Liang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Hai Liang. Hai Liang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Cheng, Edmund W., et al.. (2024). How institutionalized feedback works: Online citizen complaints and local government responsiveness in China. Governance. 38(2). 3 indexed citations
2.
Liang, Hai, et al.. (2023). I cue you liking me: Causal and spillover effects of technological engagement bait. Computers in Human Behavior. 148. 107864–107864. 2 indexed citations
3.
Liang, Hai, et al.. (2023). The effect of affordance on deliberation when retweeting: From the perspective of expression effect. Computers in Human Behavior. 151. 108010–108010. 2 indexed citations
4.
Lu, Shuning & Hai Liang. (2023). Reactance to Uncivil Disagreement?. Journal of Media Psychology Theories Methods and Applications. 36(1). 15–26. 1 indexed citations
5.
Lu, Shuning, Hai Liang, & Gina Masullo Chen. (2022). Selective Avoidance: Understanding How Position and Proportion of Online Incivility Influence News Engagement. Communication Research. 50(4). 387–409. 8 indexed citations
6.
7.
Lee, Francis, Edmund W. Cheng, Hai Liang, Gary Tang, & Samson Yuen. (2021). Dynamics of Tactical Radicalisation and Public Receptiveness in Hong Kong’s Anti-Extradition Bill Movement. Journal of Contemporary Asia. 52(3). 429–451. 44 indexed citations
8.
Liang, Hai & Francis Lee. (2021). Opinion leadership in a leaderless movement: discussion of the anti-extradition bill movement in the ‘LIHKG’ web forum. Social movement studies. 22(5-6). 670–688. 30 indexed citations
9.
Liang, Hai & Xinzhi Zhang. (2021). Partisan Bias of Perceived Incivility and its Political Consequences: Evidence from Survey Experiments in Hong Kong. Journal of Communication. 71(3). 357–379. 20 indexed citations
10.
Lee, Francis, Hai Liang, & Gary Tang. (2019). Online Incivility, Cyberbalkanization, and the Dynamics of Opinion Polarization During and After a Mass Protest Event. International journal of communication. 13. 20. 15 indexed citations
11.
Fung, Isaac Chun‐Hai, Elizabeth B Blankenship, Carmen H. Duke, et al.. (2019). Public Health Implications of Image-Based Social Media: A Systematic Review of Instagram, Pinterest, Tumblr, and Flickr. The Permanente Journal. 24(1). 71 indexed citations
12.
Liang, Hai, Isaac Chun‐Hai Fung, Zion Tsz Ho Tse, et al.. (2019). How did Ebola information spread on twitter: broadcasting or viral spreading?. BMC Public Health. 19(1). 438–438. 65 indexed citations
13.
Schaible, Braydon, Kassandra Snook, Jingjing Yin, et al.. (2019). Twitter Conversations and English News Media Reports on Poliomyelitis in Five Different Countries, January 2014 to April 2015. The Permanente Journal. 23(3). 2 indexed citations
14.
Yin, Jingjing, A. Jackson, Zion Tsz Ho Tse, et al.. (2018). World Pneumonia Day 2011–2016: Twitter contents and retweets. International Health. 11(4). 297–305. 7 indexed citations
15.
Liang, Hai. (2018). Broadcast Versus Viral Spreading: The Structure of Diffusion Cascades and Selective Sharing on Social Media. Journal of Communication. 68(3). 525–546. 38 indexed citations
16.
Liang, Hai & Fei Shen. (2018). Birds of a schedule flock together: Social networks, peer influence, and digital activity cycles. Computers in Human Behavior. 82. 167–176. 16 indexed citations
17.
Fu, King‐Wa, et al.. (2016). How people react to Zika virus outbreaks on Twitter? A computational content analysis. American Journal of Infection Control. 44(12). 1700–1702. 103 indexed citations
18.
Liang, Hai & Fei Shen. (2015). Communicative Inequalities in Online Political Discussion: A Study of Discussion Forums from 54 Societies. SSRN Electronic Journal. 1 indexed citations
19.
Liang, Hai & King‐Wa Fu. (2015). Testing Propositions Derived from Twitter Studies: Generalization and Replication in Computational Social Science. PLoS ONE. 10(8). e0134270–e0134270. 32 indexed citations
20.
Shen, Fei & Hai Liang. (2014). Cultural Difference, Social Values, or Political Systems? Predicting Willingness to Engage in Online Political Discussion in 75 Societies. International Journal of Public Opinion Research. 27(1). 111–124. 11 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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