By filtering based on who the accounts were following, I collected a list of accounts that followed at least 10 of the pre-compiled list of influencer accounts.

This yielded a list of just over 900 accounts.

By filtering on strings in description and name fields, I collected lists of accounts self-associating with Tommy Robinson.

This yielded a list of just over 2,000 accounts. Filtering by account creation date, in order to find recently created accounts.For this experiment, I filtered the objects into a few different groups: I ran the crawler for just a couple of days (between April 2nd and 3rd 2019), during which it collected about 260,000 “interesting” Twitter user objects.

The crawler was seeded with a couple of random troll accounts I found while browsing Twitter.Īs with any Twitter account crawl, the process never finishes – the length of the queue grows faster than it can be consumed. Also, save a node-edge graph representation of the network, as it is crawled.If any of the above matched, add the account name to the queue (if it isn’t already on it, and if we haven’t already queried the account), and save the user object for later processing steps.This list was collected over several months by hand-visiting Twitter accounts identified through data analysis and manual research. Check the list of accounts followed against a pre-compiled list of roughly 500 influencer accounts.In this experiment, string searches included things like “yellowvest”, “hate the eu”, “istandwithtommy”, “voted brexit”, “qanon”, and “maga”.For each user object, run a set of string matches and regular expressions against text fields (name, description, screen_name).Gather Twitter user objects of the accounts the target is following.Pull a new target account from a queue of targets.I wrote a simple crawler using the Twitter API. Step 1: Collect a list of candidate accounts I will illustrate how I have applied this methodology in an attempt to discover political disinformation around pro-leave brexit topics, and to hopefully further clarify how much overlap exists between accounts promoting far-right ideology in the US, and accounts pushing pro-leave ideology in the UK.

In this article, I’d like to share methodology I’ve been developing to observe “behind the scenes” amplification on Twitter. This post is a follow-up to F-Secure’s recent report about brexit-related amplification. As such, I’ve been trying to determine how attackers game these systems. Social networks fall into this category – they’re powered by recommendation algorithms (often based on machine learning techniques) that process large amounts of data in order to display relevant information to users. As part of the Horizon 2020 SHERPA project, I’ve been studying adversarial attacks against smart information systems (systems that utilize a combination of big data and machine learning).

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