Abstract: Our work represents another step into the detection and prevention of these
ever-more present political manipulation efforts. We, therefore, start by
focusing on understanding what the state-of-the-art approaches lack -- since
the problem remains, this is a fair assumption. We find concerning issues
within the current literature and follow a diverging path. Notably, by placing
emphasis on using data features that are less susceptible to malicious
manipulation and also on looking for high-level approaches that avoid a
granularity level that is biased towards easy-to-spot and low impact cases.
We designed and implemented a framework -- Twitter Watch -- that performs
structured Twitter data collection, applying it to the Portuguese
Twittersphere. We investigate a data snapshot taken on May 2020, with around 5
million accounts and over 120 million tweets (this value has since increased to
over 175 million). The analyzed time period stretches from August 2019 to May
2020, with a focus on the Portuguese elections of October 6th, 2019. However,
the Covid-19 pandemic showed itself in our data, and we also delve into how it
affected typical Twitter behavior.
We performed three main approaches: content-oriented, metadata-oriented, and
network interaction-oriented. We learn that Twitter's suspension patterns are
not adequate to the type of political trolling found in the Portuguese
Twittersphere -- identified by this work and by an independent peer - nor to
fake news posting accounts. We also surmised that the different types of
malicious accounts we independently gathered are very similar both in terms of
content and interaction, through two distinct analysis, and are simultaneously
very distinct from regular accounts.