Telegram Network Visualization — Tracing Forwards and Mentions

Telegram Network Visualization — Tracing Forwards and Mentions

Telegram reported a surge in active users after WhatsApp’s new terms of services raised privacy concerns. This article will explore avenues of getting public Telegram data without the need for account sign-ups, and the use of graph visualization to discover information flow between Telegram channels.

Telegram reported a surge in active users after WhatsApp’s new terms of services raised privacy concerns. This article will explore avenues of getting public Telegram data without the need for account sign-ups, and the use of graph visualization to discover information flow between Telegram channels.

A tweet from Pavel Durov, founder of the Telegram Messenger, showing a surge in new account sign-ups.

Public Telegram channels are viewable without signing up for a Telegram account. There are many online Telegram archives that perform searches for Telegram channels. Let’s use this Telegram Explorer to search for cats, and pick the channel Kitty Cutie Pie, with channel ID pet_me_feed_me.

If the channel is a public one, there may be a Preview Channel option. This allows us to view the channel on a web browser without signing up for a Telegram account. Data such as profile picture, channel description, channel member count, message content, message posting time, and message view count are openly-available.

Channels are linked up through Forwards and Mentions. The screengrab on the bottom left shows a forwarded message from the Eduji Furchan channel, while the screengrab on the bottom right shows a mention of the _World_of_Puppy _channel.

Tracing out forwarded messages and mentions could help detect communities across Telegram channels and identify the source origin of content.

Prepping Data for Gephi

Gephi is a free online graph visualization tool that comes pre-packaged with layout algorithms and network metric calculations.

To ingest CSV files, Gephi requires data to contain the following column names, which are case sensitive.

  • Node.csv — Id, Label
  • Edge.csv — Source, Target

Other attributes may be added. For example, the column ‘Size’ (which represents that Telegram channel member count) is added to the node table. Load the CSV files under the Data Laboratory tab.

Perform the following transformations in the _Overview _Tab.

  • Layout: Choose an algorithm (e.g. _OpenOrd) _to arrange nodes in clusters.
  • Statistics: Run the _Modularity _function for cluster detection, and color the nodes based on the modularity class.
  • Node Labels: Turn on node labels, and play around with different fonts to display Chinese, Russian, Arabic characters, where necessary.
  • Node Size and Label Size: Scale node size and label size based on a calculated network metric (e.g. degree).
  • Readjustment: Run the Nooverlap and Lael Adjust algorithm so that nodes and labels do not overlap.

Head over to the _Preview _tab for the final aesthetic touch.

  • Opacity: Decreasing the opacity of nodes and edges helps improve the readability of labels.
  • Scaling: Label size and Edge thickness can be scaled.
  • Export: For a higher resolution screenshot, increase the pixels before doing a PNG export. You may have to increase Gephi memory here, to support exports of a higher pixel count.

Let us replicate these steps to analyze Telegram channels for a recent case.

The QAnon community is active on Telegram, spreading conspiracy theories and distorting narrative pieces that may appear to be aligned with their ideologies.

data-analysis social-media data-science data-visualization telegram

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