Activism today is no longer bound to be organization-led, but has evolved to be mostly crowd-enabled connective actions via social media where much of the coordination and mobilization take place. This poses a challenge for social movement researchers to even keep track of the full set of action repertoires. In the social movement in Hong Kong in 2019, protesters have relied on Telegram, an encrypted messaging service, and other digital channels to mobilize thousands of collective actions of various scales and disseminate real-time updates on police’s anti-riot measures such as the use of tear gas. The months-long conflicts and the lack of official statistics render conventional manual data collection approaches difficult to implement. Using text-mining techniques, we extracted spatial–temporal information of the protesters’ call for actions and the police’s tear gas use in the social movement from over 12,000 messages collected from more than 100 Telegram channels operated by the activists and news media. The validation shows that the resulting datasets are more inclusive, especially small-scale actions, than manually compiled ones. Using the data, we identify a pattern of hybridized mobilization between organizational and non-organizational activists in the Anti-ELAB movement. This paper demonstrates how utilizing social media data can complement existing data collection methods and build a more-comprehensive record of collective actions with greater potential in supporting social movement research in the age of digitization.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Availability of data and materials
Data are released on the project website: https://antielabdata.jmsc.hku.hk
Lennon Wall (liannongqiang 連儂牆) is a makeshift bulletin board-like space that allows anyone to put up protest-related propaganda materials.
Human chain is an event where participants hold hands to form a chain of humans spanning across streets and neighborhoods.
We decide to adopt a stricter standard of media here for a more credible cross-evaluation later on. Only channels that are operated by the traditional news media agencies, such as television stations and newspapers, were included. Internet-based media and citizen reporting were excluded.
Precisely speaking, it stretches across five metro stations. Without an authoritative definition of neighborhoods, the surrounding of a metro station is often seen as a neighborhood in Hong Kong.
We adapted the geodesic algorithm by C. F. F. Karney (https://dx.doi.org/10.1007/s00190-012-0578-z) provided in the R package geosphere.
The figures can be downloaded from https://figshare.com/articles/ANTIELAB_social_movement_statistics/12228032
We define accessible on foot as whether one can walk from one district to another under reasonable and normal circumstances. For example, while it is possible to walk from Wong Tai Sin District to Sha Tin District, they are not regarded as accessible on foot since the journey involves hiking across a mountain.
We only retain the following types of events in Dataset 3 determined by keyword matching: assembly, human chain, lunchtime protest, protest in shopping malls, march, and memorial event. It is because these types of events are the ones that are most likely to be reported by the news media, i.e., included in the timeline.
Castells, M. (2015). Networks of outrage and hope: Social movements in the Internet age. Cambridge: John Wiley & Sons.
Lotan, G., Graeff, E., Ananny, M., Gaffney, D., Pearce, I., & Boyd, D. (2011). The Revolutions were tweeted: information flows during the 2011 tunisian and egyptian revolutions. International Journal of Communication, 5, 1375–1405.
Tufecki, Z. (2017). Twitter and tear gas: the power and fragility of networked protest. New Haven, CT: Yale University Press.
Bennett, W. L., & Segerberg, A. (2014). The logic of connective action: digital media and the personalization of contentious politics. New York, NY: Cambridge University Press.
Lee, F. L. F., Yuen, S., Tang, G., & Cheng, E. W. (2020). Hong Kong’s summer of uprising: from anti-extradition to anti-authoritarian protests. China Review, 19(4), 1–32.
Ku, A. S. (2020). New forms of youth activism—Hong Kong’s anti-extradition bill movement in the local-national-global nexus. Space and Polity, 24(1), 111–117. https://doi.org/10.1080/13562576.2020.1732201.
Kow, Y. M., Nardi, B., & Cheng, W. K. (2020). Be Water: Technologies in the Leaderless Anti-ELAB Movement in Hong Kong. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–12). Association for Computing Machinery. https://doi.org/10.1145/3313831.3376634
Loyle, C. E., Sullivan, C., & Davenport, C. (2013). The Northern Ireland research initiative: data on the troubles from 1968 to 1998. Conflict Management and Peace Science, 31(1), 94–106. https://doi.org/10.1177/0738894213501974.
Unankard, S., Li, X., & Sharaf, M. A. (2015). Emerging event detection in social networks with location sensitivity. World Wide Web, 18, 1393–1417. https://doi.org/10.1007/s11280-014-0291-3.
Sakaki, T., Okazaki, M., & Matsuo, Y. (2013). Tweet analysis for real-time event detection and earthquake reporting system development. IEEE Transactions on Knowledge and Data Engineering, 25(4), 919–931. https://doi.org/10.1109/TKDE.2012.29.
Stock, K. (2018). Mining location from social media: A systematic review. Computers, Environment and Urban Systems, 71, 209–240. https://doi.org/10.1016/j.compenvurbsys.2018.05.007.
Middleton, S. E., Kordopatis-Zilos, G., Papadopoulos, S., & Kompatsiaris, Y. (2018). Location extraction from social media: Geoparsing, location disambiguation, and geotagging. ACM Transactions on Information Systems, 36(4), 1–27. https://doi.org/10.1145/3202662.
McCarthy, J. D., & Zald, M. N. (1977). Resource mobilization and social movements: a partial theory. American Journal of Sociology, 82(6), 1212–1241.
Independent Police Complaints Council. (2020). A Thematic Study by the IPCC on the Public Order Events arising from the Fugitive Offenders Bill since June 2019 and the Police Actions in Response. Hong Kong. Retrieved from https://www.ipcc.gov.hk/en/public_communications/ipcc_thematic_study_report.html
Chan, E. Y. Y., Hung, K. K. C., Hung, H. H. Y., & Graham, C. A. (2019). Use of tear gas for crowd control in Hong Kong. The Lancet, 394(10208), 1517–1518. https://doi.org/10.1016/S0140-6736(19)32326-8.
Chau, T. H., & Wan, K. (2020). Pour (Tear) Gas on fire? Violent confrontations and anti-government backlash in Hong Kong. SSRN. Retrieved from https://ssrn.com/abstract=3557130
Olson, M. (1971). The logic of collective action: public goods and the theory of groups. Cambridge, MA: Harvard University Press.
Lee, F. L. F., & Chan, J. M. (2018). Media and protest logics in the digital era: The umbrella movement in Hong Kong. New York, NY: Oxford University Press.
Dowd, C., Justino, P., Kishi, R., & Marchais, G. (2020). Comparing ‘new’ and ‘old’ media for violence monitoring and crisis response: evidence from Kenya. Research & Politics, 7(3), 1–9. https://doi.org/10.1177/2053168020937592.
This study is supported by the Faculty of Social Sciences, The University of Hong Kong.
Conflicts of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open data access: https://antielabdata.jmsc.hku.hk/data/
About this article
Cite this article
Teo, E., Fu, Kw. A novel systematic approach of constructing protests repertoires from social media: comparing the roles of organizational and non-organizational actors in social movement. J Comput Soc Sc 4, 787–812 (2021). https://doi.org/10.1007/s42001-021-00101-3
- Social media
- Event extraction
- Location extraction
- Social movement
- Connective action