No abstract

Online messaging app Telegram has increased in popularity in recent years surpassing Twitter and Snapchat by the number of active monthly users in late 2020. The messenger has also been crucial to protest movements in several countries in 2019-2020, including Belarus, Russia and Hong Kong. Yet, to date only few studies examined online activities on Telegram and none have analyzed the platform with regard to the protest mobilization. In the present study, we address the existing gap by examining Telegram-based activities related to the 2019 protests in Hong Kong. With this paper we aim to provide an example of methodological tools that can be used to study protest mobilization and coordination on Telegram. We also contribute to the research on computational text analysis in Cantonese—one of the low-resource Asian languages,—as well as to the scholarship on Hong Kong protests and research on social media-based protest mobilization in general. For that, we rely on the data collected through Telegram’s API and a combination of network analysis and computational text analysis. We find that the Telegram-based network was cohesive ensuring efficient spread of protest-related information. Content spread through Telegram predominantly concerned discussions of future actions and protest-related on-site information (i.e., police presence in certain areas). We find that the Telegram network was dominated by different actors each month of the observation suggesting the absence of one single leader. Further, traditional protest leaders—those prominent during the 2014 Umbrella Movement,—such as media and civic organisations were less prominent in the network than local communities. Finally, we observe a cooldown in the level of Telegram activity after the enactment of the harsh National Security Law in July 2020. Further investigation is necessary to assess the persistence of this effect in a long-term perspective.

Facebook research has proliferated during recent years. However, since November 2017, Facebook has introduced a new limitation on the maximum amount of page posts retrievable through their Graph application programming interface, while there is limited documentation on how these posts are selected. This paper compares two datasets of the same Facebook page, a full dataset obtained before the introduction of the limitation and a partial dataset obtained after, and employs bootstrapping technique to assess the bias caused by the new limitation. This paper demonstrates that posts with high user engagement, Photo posts and Video posts, are over-represented, while Link posts are underrepresented. Top-term analysis reveals that there are significant differences in the most prominent terms between the full and partial dataset. This paper also reverse engineered the new application programming interface's ranking algorithm to identify the features of a post that would affect its odds of being selected. Sentiment analysis reveals that there are significant differences in the sentiment word usage between the selected and non-selected posts. This paper has significant implications for the representativeness of research that use Facebook page data collected after the introduction of the limitation.