They also found that recent Russian data releases have focussed on "pro-Kremlin, anti-opposition, and anti-Western content". Accounts originating in Turkey were created in large batches, and promoted President Erdoğan’s 2017 power consolidation, as well as Turkish interventions in Syria, both domestically and internationally. An indepth look at Egypt found that state-actors promoted a hashtag about terror in France, and mimicked an authentic French media outlet. Their research was conducted through a mixed methods approach that included temporal network analyses and statistical analysis of tweet metadata.
When networked timelines of intent on social media are cross-analyzed with timelines of newsworthy events and bilateral changes in policy and relationships between countries, strategies can begin to unfurl. Regarding Russia’s Internet Research Agency (IRA), the troll farm that was found to be attempting to influence the 2016 US election, my research with Dr Charles Kriel for NATO Defence Strategic Communications, reverse engineered the IRA’s operation on Twitter through a data science method called temporal network analysis. This method visualizes relationships between entities in a dataset over a time period. We found evidence within the networks of Twitter data that the IRA had been testing out multiple strategies to gain traction on social media, including experimentation with coordination (see Figure 1), survival of the fittest (or most followed) personas, and further polarizing already existing divides in the US.