Telegram and the anti-ELAB movement in Hong Kong: reshaping networked social movements through symbolic participation and spontaneous interaction

Su, Chris Chao; Chan, Michael; Paik, Sejin

doi:10.1080/17544750.2022.2092167

Cited by 11 publications

(8 citation statements)

References 40 publications

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“…However, they blend both channels and group messages in their analysis. In Su et al (2022), the authors also examined protests in Hong Kong by analyzing the messages from a public channel through different forms of participatory activity. Akbari and Gabdulhakov (2019) analyzed the role of the platform during the protests in Iran and how the government demanded information and private messages from Pavel Durov.…”

Section: The Role Of Telegram In Protestsmentioning

confidence: 99%

Differentiable characteristics of Telegram mediums during protests in Belarus 2020

Slobozhan

Brik

Sharma

2023

Soc. Netw. Anal. Min.

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Online activism has been pivotal for anti-government protests all around the world. A lot of existing research studies how people use social media to mobilize and coordinate protests. However, some platforms offer more than one kind of medium for communication, in particular, Telegram. We found that a lot of research literature focuses on either one or another medium or sometimes merges them together. In this research, we performed a comparative analysis of three communication mediums in this platform: channels, groups, and local chats during protests in Belarus in 2020. We first collected a dataset which follows six months of protest, which allows us to study the temporal variation of online activism. We find that in Telegram, these three modes of communication are not equally important. Furthermore, they are responsible for different types of communication. For example, in groups, people discuss significant protests that occurred nationwide. In comparison, people discuss local and small demonstrations and protests in chats. Finally, Telegram channels are very distinct from local chats and channels in terms of content. Our finding suggests that future research should consider these modes of the communication independently rather than blending them.

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Section: The Role Of Telegram In Protestsmentioning

confidence: 99%

Differentiable characteristics of Telegram mediums during protests in Belarus 2020

Slobozhan

Brik

Sharma

2023

Soc. Netw. Anal. Min.

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“…This effect can be mitigated by crawling the previously unknown (sub-)network of interest in its entirety. In many studies, this claim is implicitly made with the aim of capturing as complete a sample as possible of a particular movement in a particular location, such as the anti-ELAB movement in Hong Kong (Su et al, 2022) or movements against the COVID-19 containment measures in Ireland (Curley et al, 2022). However, incomplete snowball sampling in sparse networks might result in overlooking lower-degree nodes (e.g., channels that are rarely forwarded or mentioned in other channels) that may act as bridges between relevant subcommunities (Erickson, 1979), leading to biased results.…”

Section: Specific Decisions In Snowball Sampling and Their Effectsmentioning

confidence: 99%

“…This can limit how fast the snowball sample grows and prevent the sampling algorithm from expanding to irrel evant parts of the network. Candidate nodes for further sampling iterations can be selected manually based on actor coding (Su et al, 2022). Some studies only include channels and exclude chat groups in their sampling iterations (Su et al, 2022;Teo & Fu, 2021;Urman & Katz, 2022b).…”

Section: Specific Decisions In Snowball Sampling and Their Effectsmentioning

confidence: 99%

“…Candidate nodes for further sampling iterations can be selected manually based on actor coding (Su et al, 2022). Some studies only include channels and exclude chat groups in their sampling iterations (Su et al, 2022;Teo & Fu, 2021;Urman & Katz, 2022b). To detect the most influential accounts of the German Twittersphere in a resource-efficient way, Münch and colleagues apply the rank degree method, which only includes the most influential accounts for the subsequent sampling iteration (Münch et al, 2021).…”

Section: Specific Decisions In Snowball Sampling and Their Effectsmentioning

confidence: 99%

“…This means that seeds that are potentially situated in different clusters of the network of interest aid in their full detection. Studies interested in identifying actors located in a dense and connected cluster (Curley et al, 2022;Su et al, 2022) have a higher probability of detecting all relevant nodes with a smaller seed set than those aimed at detecting a larger, dispersed set of actors (Baumgartner et al, 2020;La Morgia et al, 2021).…”

Section: Specific Decisions In Snowball Sampling and Their Effectsmentioning

confidence: 99%

See 2 more Smart Citations

Mapping a Dark Space: Challenges in Sampling and Classifying Non- Institutionalized Actors on Telegram

Jost,

Heft,

Buehling

et al. 2023

M&K

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Crafted as an open communication platform characterized by high anonymity and minimal moderation, Telegram has garnered increasing popularity among activists operating within repressive political contexts, as well as among political extremists and conspiracy theorists. While Telegram offers valuable data access to research non-institutionalized activism, scholars studying the latter on Telegram face unique theoretical and methodological challenges in systematically defining, selecting, sampling, and classifying relevant actors and content. This literature review addresses these issues by considering a wide range of recent research. In particular, it discusses the methodological challenges of sampling and classifying heterogeneous groups of (often non-institutionalized) actors. Drawing on social movement research, we first identify challenges specific to the characteristics of non-institutionalized actors and how they become interlaced with Telegram’s platform infrastructure and requirements. We then discuss strategies from previous Telegram research for the identification and sampling of a study population through multistage sampling procedures and the classification of actors. Finally, we derive challenges and potential strategies for future research and discuss ethical challenges.

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A text mining and machine learning study on the trends of and dynamics between collective action and mental health in politically polarized online environments

Lam,

Chan

2024

J Comput Soc Sc

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Social media and online forums play an increasingly important role in the mobilization of collective action. This study examined how the discussion of collective actions impacts the expression of psychological distress in politically polarized online environments. We used text mining and machine learning models to analyze 39,487,911 user-generated comments during the 2019 social unrest in Hong Kong on two online forums frequented by anti-government (Lihkg.com) and pro-government (Discuss.com.hk) netizens. Results from time-series models yielded two main findings. First, there was a time-lagged association between the discussion of protest and the mention of psychological distress on both forums. Second, on Discuss.com.hk but not Lihkg.com, fewer comments containing psychological distress were created on days with offline protests (especially on days with violent conflicts) than days without. Together, these findings suggest that politically polarizing environments contribute to psychological distress.

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Telegram and the anti-ELAB movement in Hong Kong: reshaping networked social movements through symbolic participation and spontaneous interaction

Cited by 11 publications

References 40 publications

Differentiable characteristics of Telegram mediums during protests in Belarus 2020

Differentiable characteristics of Telegram mediums during protests in Belarus 2020

Mapping a Dark Space: Challenges in Sampling and Classifying Non- Institutionalized Actors on Telegram

A text mining and machine learning study on the trends of and dynamics between collective action and mental health in politically polarized online environments

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