Insights into using temporal coordinated behaviour to explore connections between social media posts and influence
Authors: Elisa Sartori, Serena Tardelli, Maurizio Tesconi, Mauro Conti, Stefano Cresci, Giovanni Da San Martino


Reviewer paper summaries: The paper investigates how exposure to different messages on social media can influence users to change their political affiliations. Using a dataset of tweets from the 2019 UK elections, the authors analyze how users who switched communities differed from those who didn't in terms of the messages they liked.
They find that likes provide a robust measure of community affiliation, consistent with previous analyses based on retweets and hashtags. Users who switched communities (Archetype 2) were exposed to a significantly different set of messages, characterized by distinctive linguistic features.The findings suggest that specific communication styles and persuasive strategies may play a role in shifting users' political leanings on social media.
Summary of strengths: Paper is well written and is easy to follow.
Method in the paper is well explained.
Summary of weaknesses: The tweet dataset collected by Tardelli et.al. is biased towards hashtags used to collect it. Comparing likes with hashtags, where most hashtags are collected but likes may or may not be part of the dataset makes the conclusions weak. Moreover, assigning each user to a party based on most likes could lead to errors as the dataset itself was collected on a certain set of hashtags and does not cover all the likes.

As mentioned in the limitations, the dataset of 151 user archetypes, is very limited and not generalizable. If possible, using the same method for another dataset would strengthen the arguments made.
Comments, suggestions and typos: I would also like to recommend a discussion on implications, why such finding is important and what it entails in context of social media data specifically related to elections/partisanship.
Overall assessment scores: 2