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How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

We find evidence to support common behaviour in smartphone usage based on analysis of application (app) switching. This is an overlooked aspect of smartphone usage that gives additional insight beyond screen time and the particular apps that are accessed. Using a dataset of usage behaviour from 53 participants over a 6-week period, we find strong similarity in the structure of networks built from app switching, despite diversity in the apps used, and the volume of app switching. App switch networks exhibit small-world, broad-scale network features, with a rapid popularity decay, suggesting that preferential attachment may drive next-app decision making.

As of April 2024, almost 32 percent of global Instagram audiences were aged between 18 and 24 years, and 30.6 percent of users were aged between 25 and 34 years. Overall, 16 percent of users belonged to the 35 to 44 year age group.

              Instagram users

              With roughly one billion monthly active users, Instagram belongs to the most popular social networks worldwide. The social photo sharing app is especially popular in India and in the United States, which have respectively 362.9 million and 169.7 million Instagram users each.

              Instagram features

              One of the most popular features of Instagram is Stories. Users can post photos and videos to their Stories stream and the content is live for others to view for 24 hours before it disappears. In January 2019, the company reported that there were 500 million daily active Instagram Stories users. Instagram Stories directly competes with Snapchat, another photo sharing app that initially became famous due to it’s “vanishing photos” feature.
              As of the second quarter of 2021, Snapchat had 293 million daily active users.

As of January 2024, Instagram was slightly more popular with men than women, with men accounting for 50.6 percent of the platform’s global users. Additionally, the social media app was most popular amongst younger audiences, with almost 32 percent of users aged between 18 and 24 years.

              Instagram’s Global Audience

              As of January 2024, Instagram was the fourth most popular social media platform globally, reaching two billion monthly active users (MAU). This number is projected to keep growing with no signs of slowing down, which is not a surprise as the global online social penetration rate across all regions is constantly increasing.
              As of January 2024, the country with the largest Instagram audience was India with 362.9 million users, followed by the United States with 169.7 million users.

              Who is winning over the generations?

              Even though Instagram’s audience is almost twice the size of TikTok’s on a global scale, TikTok has shown itself to be a fierce competitor, particularly amongst younger audiences. TikTok was the most downloaded mobile app globally in 2022, generating 672 million downloads. As of 2022, Generation Z in the United States spent more time on TikTok than on Instagram monthly.

Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

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Mobile Application Coverage: The 30% Curse and Ways Forward## Purpose In this artifact, we provide the information about our benchmarks used for manual and tool exploration. We include coverage results achieved by tools and human analysts as well as plots of the coverage progression over time for analysts. We further provide manual analysis results for our case study, more specifically extracted reasons for unreachability for the case study apps and extracted code-level properties, which constitute a ground truth for future work in coverage explainability. Finally, we identify a list of beyond-GUI exploration tools and categorize them for future work to take inspiration from. We are claiming available and reusable badges; the artifact is fully aligned with the results described in our paper and comprehensively documented.## ProvenanceThe paper preprint is available here: https://people.ece.ubc.ca/mjulia/publications/Mobile_Application_Coverage_ICSE2025.pdf## Data The artifact submission is organized into five parts:- 'BenchInfo' excel sheet describing our experiment dataset- 'Coverage' folder containing coverage results for tools and analysts (RQ1) - 'Reasons' excel sheet describing our manually extracted reasons for unreachability (RQ2)- 'ActivationProperties' excel sheet describing our manually extracted code properties of unreached activities (RQ3)- 'ActivationProperties-Graph' pdf which presents combinations of the extracted code properties in a graph format.- 'BeyondGUI' folder containing information about identified techniques which go beyond GUI exploration.The artifact requires about 15MB of storage.### Dataset: 'BenchInfo.xlsx'This file list the full application dataset used for experiments into three tabs: 'BenchNotGP' (apps from AndroTest dataset which are not on Google Play), 'BenchGP' (apps from AndroTest which are also on Google Play) and 'TopGP' (top ranked free apps from Google Play). Each tab contains the following information:- Application Name- Package Name- Version Used (Latest)- Original Version- # Activities- Minimum SDK- Target SDK- # Permissions (in Manifest)- List of Permissions (in Manifest)- # Features (in Manifest)- List of Features (in Manifest)The 'TopGP' sheet also includes Google-Play-specific information, namely:- Category (one of 32 app categories)- Downloads- Popularity RankThe 'BenchGP' and 'BenchNotGP' sheets also include the original version (included in the AndroTest benchmark) and the source (one of F-Droid, Github or Google Code Archives).### RQ1: 'Coverage'The 'Coverage' folder includes coverage results for tools and analysts, and is structured as follows:- 'CoverageResults.xlsx": An excel sheet containing the coverage results achieved by each human analysts and tool. - The first tab described the results over all apps for analysts combined, tools combined, and analysts + tools, which map to Table II in the paper. - Each of the following 42 tab, one per app in TopGP, marks the activities reached by Analyst 1, Analyst 2, Tool 1 (ape) and Tool 2 (fastbot), with an 'x' in the corresponding column to indicate that the activity was reached by the given agent.- 'Plots': A folder containing plots of the progressive coverage over time of analysts, split into one folder for 'Analyst1' and one for 'Analyst2'. - Each of the analysts' folder includes a subfolder per benchmark ('BenchNotGP', 'BenchGP' and 'TopGP'), containing as many png files as applications in the benchmark (respectively 47, 14 and 42 image files) named 'ANALYST_[X]_[APP_PACKAGE_NAME]'.png.### RQ2: 'Reasons.xslx'This file contains the extracted reasons for unreachability for the 11 apps manually analyzed. - The 'Summary' tab provides an overview of unreached activities per reasons over all apps and per app, which corresponds to Table III in the paper. - The following 11 tabs, each corresponding to and named after a single application, describe the reasons associated with each activity of that application. Each column corresponds to a single reason and 'x' indicates that the activity is unreached due to the reason in that column. The top row sums up the total number of activities unreached due to a given reason in each column.- The activities at the bottom which are greyed out correspond to activities that were reached during exploration, and are thus excluded from the reason extraction.### RQ3: 'ActivationProperties.xslx'This file contains the full list of activation properties extracted for each of the 185 activities analyzed for RQ2.The first half of the columns (columns C-M) correspond to the reasons (excluding Transitive, Inconclusive and No Caller) and the second half (columns N-AD) correspond to properties described in Figure 5 in the paper, namely:- Exported- Activation Location: - Code: GUI/lifecycle, Other Android or App-specific - Manifest- Activation Guards: - Enforcement: In Code or In Resources - Restriction: Mandatory or Discretionary- Data: - Type: Parameters, Execution Dependencies - Format: Primitive, Strings, ObjectsThe rows are grouped by applications, and each row correspond to an activity of that application. 'x' in a given column indicates the presence of the property in that column within the analyzed path to the activity. The third and fourth rows sums up the numbers and percentages for each property, as reported in Figure 5.### RQ3: 'ActivationProperties-Graph.pdf'This file shows combinations of the individual properties listed in 'ActivationProperties.xlsx' in a graph format, extending the combinations described in Table IV with data (types and format) and reasons for unreachability.### BeyondGUIThis folder includes:- 'ToolInfo.xlsx': an excel sheet listing the identified 22 beyond-GUI papers, the date of publication, availability, invasiveness (Source code, Bytecode, framework, OS) and their targeting strategy (None, Manual or Automated).- ToolClassification.pdf': a pdf file describing our paper selection methodology as well as a classication of the techniques in terms of Invocation Strategy, Navigation Strategy, Value Generation Strategy, and Value Generation Types. We fully introduced these categories in the pdf file.## Requirements & technology skills assumed by the reviewer evaluating the artifactThe artifact entirely consists of Excel sheets which can be opened with common Excel visualization software, i.e., Microsoft Excel, coverage plots as PNG files and PDF files. It requires about 15MB of storage in total.No other specific technology skills are required of the reviewer evaluating the artifact.

The dataset contains all publicly accessible green spaces in the city of Heidelberg including an attribute table with three main indicators on the use and perception of urban green spaces (Popularity_Indicator, Aesthetics_Indicator and Animals_Indicator) derived from social media. In addition to these three main values, the attribute table contains a further 18 statistical values, which were calculated by intersecting the green areas with classified social media data and are documented in the metadata description. The green space polygons were generated using an automatic approach described in Ludwig et al. (2021) is described in more detail. The green spaces and indicator values ​​are part of the central database (Cakir et al., 2021) for the evaluation of green spaces in Heidelberg according to criteria or suitability for specific activities using the meinGrün app (app.meingruen.org). The popularity of urban green spaces in Heidelberg was measured by the density of location-related social media posts. The processing of the data for green spaces is presented and described in a notebook (pub.zih.tu-dresden.de/~s7398234/vis/zielgeometrien-intersect_v6.html) The aesthetics indicator describes the aesthetic value of urban green spaces in Heidelberg and was based on conceptualized and measured the density of aesthetics-related social media posts. For the identification of the social media posts related to the aesthetic value of urban green spaces, a novel methodology based on unsupervised text classification and targeted filtering of social media posts was developed and in Gugulica & Burghardt, 2021 - work in progress - is described in more detail. The animals indicator shows the presence of wild animals in urban green spaces in Heidelberg. The quantification of the wildlife indicator is based on the underlying assumption that densities of social media posts related to wildlife and wildlife photography potentially reflect demand for wildlife viewing and indicate hotspots for that activity. In order to identify the relevant social media posts for the calculation of the indicator, the above methodology, based on unsupervised text classification and targeted filtering of social media posts and described in more detail in Gugulica & Burghardt, 2021 - in progress - was used , used. Location-aware social media data from Instagram, Flickr, and Twitter (including photos annotated with text and text messages) were used to quantify the popularity, aesthetics, and wildlife indicators of urban green spaces in Heidelberg. The data was identified using the embedded location information and a custom bounding box, and retrieved and collected via the API provided by each of the platforms. Only publicly available social media posts published between January 1, 2015 and October 31, 2020 were considered and saved as a CSV file along with meta information such as user ID, coordinates, captions, recording and uploading date saved. Duplicates were removed and after overlaying the dataset with the target polygons, the final dataset for Heidelberg comprised 308,496 posts (28,886 tweets, 245,992 Instagram posts, and 33,618 Flickr posts). The choice of platforms was mainly determined by the popularity of the social media channels and the specificity of the respective content. In order to cover a broader range of users, the three data sources were combined, which led to more robust results due to the increased data width. References: Cakir, S., Schorcht, M., Stanley, C., Theodor, R., Ludwig, C., Gugulica, M., Dunkel, A., & Hecht, R. (2021). Urban green spaces and indicators: Heidelberg (2021 version) [Data set]. Leibniz Institute of Ecological Urban and Regional Development, Weberplatz 1, 01217 Dresden, Germany. https://doi.org/10.26084/IOERFDZ-DATA-DE-2021-2 Ludwig, C.; Hecht, R.; Lautenbach, S.; Schorcht, M.; Zipf, A. (2021): Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions. In: ISPRS International Journal of Geo-Information 10 (2021) 4, p.251 https://doi.org/10.3390/ijgi10040251

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The most successful music streaming service in the United States was Apple Music as of September, with the most up to date information showing that 49.5 million users accessed the platform each month. Spotify closely followed, with a similarly impressive 47.7 million monthly users.

What is a music streaming service?

Music streaming services provide their users with a database compiled of songs, playlists, albums and videos, where content can be accessed online, downloaded, shared, bookmarked and organized.

The music streaming business is huge, and has sometimes been lauded as the savior of the music industry. The biggest two services are in constant competition for the monopoly of the market. Apple Music was launched in 2015, whereas Spotify has been around since 2008. Other popular streaming services include Deezer, SoundCloud and iHeartRadio.

Do artists make a lot of money from streaming services? 

In short, unfortunately not. Both Apple Music and Spotify have been frequently criticized for the tiny royalty payments they offer artists. Particularly for emerging talent, streaming services are far from a lucrative source of income. Bigger, established stars like Taylor Swift are more likely to regularly make a good amount of money this way. But either way, a track needs to go viral or be streamed several million times before it earns any real cash.

In June 2025, Roblox was the most-downloaded gaming app in the Google Play Store worldwide. The creative gaming platform generated more than 21.28 million downloads from Android users. Block Blast! was the second-most popular gaming app title with approximately 19.9 million downloads from global users.

The dataset contains all publicly accessible green spaces in the city of Dresden including an attribute table with three main indicators on the use and perception of urban green spaces (Popularity_Indicator, Aesthetics_Indicator and Animals_Indicator) derived from social media. In addition to these three main values, the attribute table contains a further 18 statistical values, which were calculated by intersecting the green areas with classified social media data and are documented in the metadata description. The green space polygons were generated using an automatic approach described in Ludwig et al. (2021) is described in more detail. The green spaces and indicator values ​​are part of the central database (Cakir et al., 2021) for the evaluation of green spaces in Dresden according to criteria or suitability for certain activities using the meinGrün app (app.meingruen.org). The popularity of urban green spaces in Dresden was measured by the density of location-related social media posts. The processing of the data for green spaces is presented and described in a notebook (pub.zih.tu-dresden.de/~s7398234/vis/zielgeometrien-intersect_v6.html). The aesthetics indicator describes the aesthetic value of urban green spaces in Dresden and was conceptualized and measured based on the density of aesthetics-related social media posts. For the identification of the social media posts related to the aesthetic value of urban green spaces, a novel methodology based on unsupervised text classification and targeted filtering of social media posts was developed and in Gugulica & Burghardt, 2021 - work in progress - is described in more detail. The animals indicator shows the presence of wild animals in urban green spaces in Dresden. The quantification of the wildlife indicator is based on the underlying assumption that densities of social media posts related to wildlife and wildlife photography potentially reflect demand for wildlife viewing and indicate hotspots for that activity. In order to identify the relevant social media posts for the calculation of the indicator, the above methodology, based on unsupervised text classification and targeted filtering of social media posts and described in more detail in Gugulica & Burghardt, 2021 - in progress - was used , used. For quantifying the popularity, aesthetics and wildlife indicators of urban green spaces in Dresden, location-aware social media data from Instagram, Flickr and Twitter (including photos annotated with text and text messages) was used. The data was identified using the embedded location information and a custom bounding box, and retrieved and collected via the API provided by each of the platforms. Only publicly available social media posts published between January 1, 2015 and October 31, 2020 were considered and saved as a CSV file along with meta information such as user ID, coordinates, captions, recording and uploading date saved. Duplicates were removed and after overlaying the dataset with the target polygons, the final dataset for Dresden included 782,310 social media posts (59,101 tweets, 664,925 Instagram posts, and 58,284 Flickr posts). The choice of platforms was mainly determined by the popularity of the social media channels and the specificity of the respective content. In order to cover a broader range of users, the three data sources were combined, which led to more robust results due to the increased data width. References: Cakir, S., Schorcht, M., Stanley, C., Rieche, T., Ludwig, C., Gugulica, M., Dunkel, A., Hecht, R. (2021). Urban green spaces and indicators: Dresden (2021 version) [Data set]. Leibniz Institute of Ecological Urban and Regional Development, Weberplatz 1, 01217 Dresden, Germany. https://doi.org/10.26084/IOERFDZ-DATA-DE-2021-1 Ludwig, C.; Hecht, R.; Lautenbach, S.; Schorcht, M.; Zipf, A. (2021): Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions. In: ISPRS International Journal of Geo-Information 10 (2021) 4, p.251 https://doi.org/10.3390/ijgi10040251

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Mobile mindfulness interventions represent a promising alternative to traditional in-person interventions that are resource demanding and have limited accessibility, preventing use by many populations. Despite greater accessibility and popularity of mobile mindfulness applications (apps), research is needed testing the effectiveness of brief interventions delivered via these platforms. The present study assessed the efficacy of a brief mobile mindfulness intervention compared to an active control for increasing state and trait mindfulness and improving mood, as well as the acceptability of the app, in a sample of undergraduate students. Participants (N=139; Mage=19.43years, 80.6% female, 83.5% White) were randomly assigned to either a 10-day mobile mindfulness (Headspace) or cognitive training (Peak) condition. Trait mindfulness was measured pre- and post-intervention. During the 10-day intervention, participants completed 10-min daily exercises on the assigned app, responded to daily questionnaires of state mindfulness and mood, and completed a daily written log of their reactions to the app exercises. Attrition was low (90% completion rate) and did not differ by condition. Participants in the mindfulness condition spent an average of 88.15min (SD=24.75) meditating out of the full 100min prescribed by the intervention. State mindfulness significantly increased across the 10-day intervention for participants in the mindfulness, but not the cognitive training, condition beginning around days 5 and 6. Some aspects of trait mindfulness increased and mood improved from pre- to post-intervention, but these changes were observed in both conditions (i.e., no significant differences were observed by condition). Qualitative analysis of open-ended reactions to the mindfulness app indicated that participants reported more likes than dislikes. Common themes for likes were that participants experienced feelings of calm and focus following the daily mindfulness exercises. Dislikes included discomfort and anxiety associated with increased awareness of thoughts and physical sensations. These findings suggest that while a brief mobile mindfulness intervention is acceptable to undergraduate college students and effective at increasing state mindfulness, a longer intervention may be needed in order to elicit corresponding changes in trait-level mindfulness or mood.

In the fourth quarter of 2024, TikTok generated around 186 million downloads from users worldwide. Initially launched in China first by ByteDance as Douyin, the short-video format was popularized by TikTok and took over the global social media environment in 2020. In the first quarter of 2020, TikTok downloads peaked at over 313.5 million worldwide, up by 62.3 percent compared to the first quarter of 2019.

              TikTok interactions: is there a magic formula for content success?

              In 2024, TikTok registered an engagement rate of approximately 4.64 percent on video content hosted on its platform. During the same examined year, the social video app recorded over 1,100 interactions on average. These interactions were primarily composed of likes, while only recording less than 20 comments per piece of content on average in 2024.
              The platform has been actively monitoring the issue of fake interactions, as it removed around 236 million fake likes during the first quarter of 2024. Though there is no secret formula to get the maximum of these metrics, recommended video length can possibly contribute to the success of content on TikTok.
              It was recommended that tiny TikTok accounts with up to 500 followers post videos that are around 2.6 minutes long as of the first quarter of 2024. While, the ideal video duration for huge TikTok accounts with over 50,000 followers was 7.28 minutes. The average length of TikTok videos posted by the creators in 2024 was around 43 seconds.

              What’s trending on TikTok Shop?

              Since its launch in September 2023, TikTok Shop has become one of the most popular online shopping platforms, offering consumers a wide variety of products. In 2023, TikTok shops featuring beauty and personal care items sold over 370 million products worldwide.
              TikTok shops featuring womenswear and underwear, as well as food and beverages, followed with 285 and 138 million products sold, respectively. Similarly, in the United States market, health and beauty products were the most-selling items,
              accounting for 85 percent of sales made via the TikTok Shop feature during the first month of its launch. In 2023, Indonesia was the market with the largest number of TikTok Shops, hosting over 20 percent of all TikTok Shops. Thailand and Vietnam followed with 18.29 and 17.54 percent of the total shops listed on the famous short video platform, respectively.

🏋️ About This Dataset This comprehensive fitness dataset contains over 600,000 structured workout routines and exercise entries scraped from fitness planning platform data. The dataset includes both detailed exercise-level data and program-level summaries, making it ideal for building recommendation systems, analyzing workout patterns, and understanding fitness program structures.

📊 Dataset Overview Two complementary files: 1. Main Dataset (fitness_exercises.csv): 605,033 individual exercise entries with detailed workout information 2. Program Summary (program_summary.csv): 2,598 unique fitness programs with aggregated metadata

🔑 Key Features

Main Dataset (605K+ rows): - Exercise details: name, sets, reps, intensity - Program structure: week, day, time per workout - User targeting: fitness level, goals, equipment needs - Temporal data: creation and edit timestamps - Program metadata: length, number of exercises per workout

Program Summary (2.6K+ programs): - Program overview: title, description, fitness level - Target goals and equipment requirements - Program duration and workout timing - Total exercise count per program - Creation and modification timestamps

🎯 Use Cases - Building workout recommendation systems - Analyzing fitness program effectiveness and popularity - Understanding exercise patterns and program structures - Creating personalized workout generators - Fitness app development and research - Program-level analysis and clustering

🔧 Technical Details - Format: CSV files - Combined size: ~300MB+ - Data quality: Minimal missing values (<1% for most columns) - Collection period: [Add your scraping date] - Source: Fitness platform data (with attribution)

📝 Data Dictionary

Main Dataset: - title: Workout/program name - description: Detailed workout description - level: Fitness level (beginner/intermediate/advanced) - goal: Primary fitness objective - equipment: Required equipment type - program_length: Duration in weeks - time_per_workout: Duration per session (minutes) - week/day: Position in program structure - exercise_name: Specific exercise name - sets/reps: Exercise volume parameters (negative values are time in seconds) - intensity: Exercise intensity level

Program Summary: - title: Program name - description: Program overview and objectives - level: Target fitness level - goal: Primary fitness goal - equipment: Required equipment - program_length: Total program duration (weeks) - time_per_workout: Average workout duration (minutes) - total_exercises: Total number of exercises in program - created/last_edit: Program timestamps

🔗 Data Relationship The program_summary file provides aggregated views of the detailed exercise data, allowing for both micro-level exercise analysis and macro-level program insights.

⚖️ Important Notes - Data collected from publicly available fitness planning platform - Cleaned and structured for research/educational use - Please respect original platform's terms of service - Consider this for non-commercial research and educational purposes

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.