Casm: A deep-learning approach for identifying collective action events with text and image data from social media H Zhang, J Pan Sociological Methodology 49 (1), 1-57, 2019 | 210 | 2019 |
Addressing selection bias in event studies with general-purpose social media panels H Zhang, S Hill, D Rothschild Journal of Data and Information Quality (JDIQ) 10 (1), 1-24, 2018 | 25* | 2018 |
Image clustering: An unsupervised approach to categorize visual data in social science research H Zhang, Y Peng Sociological Methods & Research 53 (3), 1534-1587, 2024 | 24 | 2024 |
Authoritarian responsiveness and political attitudes during COVID-19: evidence from Weibo and a survey experiment L Wei, E Yao, H Zhang Computational Social Science, 52-88, 2024 | 10 | 2024 |
The challenges of “more data” for protest event analysis H Zhang, J Pan Sociological Methodology 49 (1), 76-82, 2019 | 8 | 2019 |
Physical exposures to political protests impact civic engagement: Evidence from 13 quasi-experiments with chinese social media H Zhang Available at SSRN 2647222, 2016 | 8 | 2016 |
How using machine learning classification as a variable in regression leads to attenuation bias and what to do about it H Zhang SocArXiv, accessed at https://osf. io/preprints/socarxiv/453jk/on May 2, 2022, 2021 | 6 | 2021 |
Robots and protest: does increased protest among Chinese workers result in more automation? L Liu, H Zhang Socio-Economic Review 21 (3), 1751-1772, 2023 | 4 | 2023 |
Selection and Description Bias in Protest Reporting by Government and News Media on Weibo H Zhang, Y Lu, R Bai The China Quarterly 257, 75-99, 2024 | 3 | 2024 |
Analyzing Image Data with Machine Learning H Zhang | | 2023 |
Mobilizing Without the Masses: Control and Contention in China [Book review] H Zhang Mobilization: an International Quarterly, 153, 2020 | | 2020 |