Applying Word Embeddings to Measure Valence in Information Operations Targeting Journalists in Brazil
This work addresses the challenge of measuring subtle influence in online discourse for researchers and policymakers, though it is incremental as it builds on existing word embedding methods.
The paper tackled the problem of detecting subtle biases in information operations targeting journalists in Brazil by developing a measure to assess changes in overall valence towards specific actors, with preliminary results showing that known campaigns target female journalists more than male journalists and leave detectable traces in Twitter discourse.
Among the goals of information operations are to change the overall information environment vis-á-vis specific actors. For example, "trolling campaigns" seek to undermine the credibility of specific public figures, leading others to distrust them and intimidating these figures into silence. To accomplish these aims, information operations frequently make use of "trolls" -- malicious online actors who target verbal abuse at these figures. In Brazil, in particular, allies of Brazil's current president have been accused of operating a "hate cabinet" -- a trolling operation that targets journalists who have alleged corruption by this politician and other members of his regime. Leading approaches to detecting harmful speech, such as Google's Perspective API, seek to identify specific messages with harmful content. While this approach is helpful in identifying content to downrank, flag, or remove, it is known to be brittle, and may miss attempts to introduce more subtle biases into the discourse. Here, we aim to develop a measure that might be used to assess how targeted information operations seek to change the overall valence, or appraisal, of specific actors. Preliminary results suggest known campaigns target female journalists more so than male journalists, and that these campaigns may leave detectable traces in overall Twitter discourse.