Applying Word Embeddings to Measure Valence in Information Operations Targeting Journalists in Brazil. (arXiv:2201.02257v1 [cs.CL])

Among the goals of information operations are to change the overall
information environment vis-‘a-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.



Related post