The most complete open-source tool for Twitter intelligence analysis


tinfoleak is an open-source tool within the OSINT (Open Source Intelligence) and SOCMINT (Social Media Intelligence) disciplines, that automates the extraction of information on Twitter and facilitates subsequent analysis for the generation of intelligence. Taking a user identifier, geographic coordinates or keywords, tinfoleak analyzes the Twitter timeline to extract great volumes of data and show useful and structured information to the intelligence analyst.

tinfoleak is included in several places: KaliCAINEBlackArch and Buscador. It is currently the most comprehensive open-source tool for intelligence analysis on Twitter.

tinfoleak can extract the following information:

  • Account info / User Activity / Protected Accounts / User Relations
  • Source Applications / User Devices / Use Frequency
  • Hashtags / Mentions / Likes
  • Text Analysis / Words Frequency / Media / Metadata
  • User Visited Places / User Routes / User Top Locations
  • Social Networks / Digital Identities
  • Geolocated Users / Tagged Users
  • Followers / Friends
  • Lists / Collections
  • Conversations

Github: https://github.com/vaguileradiaz/tinfoleak


Screen Shot 2019-03-17 at 4.47.04 PM

Above is the opening screen. Below is a basic configuration searching for my twitter handle. Be careful, as the Twitter API is very sensitive. I suggest running several different scans on your target, selecting different criteria — don’t forget to change the name of your output file.

Screen Shot 2019-03-17 at 4.51.06 PM

Here are some shots of the output HTML file. This is just a short report, but as you will be able to see, the report can turn out pretty long if you were to enable everything and request 50 of every thing selected.

Screen Shot 2019-03-17 at 4.53.26 PM

Each of the below buttons across the top are hot links to each section which represents each configuration option you enable. Again, you have to be careful with the Twitter API. There is a ton of data flowing through it and it will crash the application if too much data is requested at once. Split your searches and name your output files to match what is searched.

Screen Shot 2019-03-17 at 4.54.50 PM

From a user we can get all this information:

  • Basic information of the account
  • Identification of devices, operating systems, applications and social networks used
  • Themes on which the user thinks
  • Highest activity time zones, sleep schedules
  • User contacts and relationships between them (friends, family, colleagues …)
  • User opinion on specific topics
  • Download of published images
  • Geolocation of tweets (including photographs)
  • Identification of habitual residence, place of work and other relevant locations
  • Predicting future locations
  • Analysis of metadata
  • Themes related to a keyword

From the coordinates:

  • Users who were in the area, on a specific day and time
  • Images and videos of events and events

From the Global Time Line

  • Tweets on specific topics that were published on a specific date or period

As you see with all this information it is easy to reach goals we set for learning about a target.


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