Tool address: https://github.com/sockysec/Telerecon
This Python tool covers a wide range of investigable points in TG. Using the script will save a lot of time. This script can not only capture messages, groups, or channel collections, but also has some very good analysis options. It can provide in-depth understanding of relationships and interactions, locations mentioned in chats, names, even posting patterns, and even EXIF metadata geolocation.
Telerecon is a comprehensive OSINT reconnaissance framework for researching, investigating, and capturing Telegram.
For example, by entering a target username, Telerecon can effectively capture in multiple chats, collect personal profile metadata, account activity, user messages, extract potential selectors, ideological indicators, identify named entities, build possible networks of associated individuals, and EXIF metadata geolocation maps, as well as various other analyses.
Other features of Telerecon include capturing Telegram channels/groups, automatic forward mapping for exploratory network analysis, and conducting channel community surveys.
Extended features of the tool:
Get user information: Search for @username and return any public user information (username, first name, last name, phone number, user ID, bio, online status, profile picture).
Check user activity in channel lists: Traverse the txt/csv directory list of Telegram channels and find any messages from the target username. (Assuming the directory list is in the Telerecon main directory).
Collect user messages from target channels: Collect and compile any messages from the target username in the target channel. Media can also be optionally downloaded (note: media downloads will slow down the collection speed).
Collect user messages from target channel lists: Traverse the txt/csv directory list of Telegram channels and collect and compile any messages from the target username. Media can also be optionally downloaded (note: media downloads will slow down the collection speed). Assuming the directory list is in the Telerecon main directory.
Capture all messages in a channel: Collect and compile messages from the target channel. Download the complete history, the last 24 hours, or a custom date range.
Capture all t.me URLs from a channel: Parse a channel and extract all t.me URLs mentioned in it. This is for easy creation of a Telegram directory.
Scrape forward relationships to target channel: Scrape forward relationships to the target channel. Export optimized adjacency lists for Gephi and a directory of discovered channel URLs.
Scrape forward relationships to target channel lists: Traverse the txt/csv directory list of Telegram channels and scrape forward relationships. Export optimized adjacency lists for Gephi and a directory of discovered channel URLs. The output can be merged using terminal commands (i.e., merge URL lists = cat*.csv | sort | uniq>combined.csv).
Identify possible user associations through interactive network mapping: Assuming user messages have been collected. Build a network visualization showing replies/interactions with other users (helps identify possible associations).
Analyze user messages to extract selectors/intel: Output a report containing any potential phone numbers, emails, or other selectors extracted based on regular expressions and target keywords (the report includes citations for verification). The target keywords can be customized by editing the script.
Extract GPS data from collected user media: Assuming user messages have been collected. Create a compiled spreadsheet of EXIF metadata extracted from all images, as well as a map visualization showing any extracted GPS metadata.
Create a visual report based on collected user messages: Assuming user messages have been collected. Create a comprehensive analysis report showing postage patterns of users over time (useful for lifestyle analysis, etc.).
Extract named entities from collected user messages: Assuming user messages have been collected. Create a report containing extracted personnel, organization, location, and date entities through named entity recognition. Although this feature is not perfect, it can be used to identify key entities for further investigation in large datasets.
Conduct a subscriber survey in the target channel list: Traverse the txt/csv directory list of Telegram channels and report the number of subscribers/members.
Analyze ideological indicators in user messages: Assuming user messages have been collected. Output a report containing key phrases that may indicate ideology (the report includes citations for verification). The key phrases can be customized by editing the script. The default function parses the text to detect hate speech/racism, white identity motivated extremism, conspiracy ideas, sovereign citizens, and incel terms. Note: Context is crucial, mentioning a keyword does not imply ideological motivation for the user. However, this feature is still useful for quickly assessing targets.