Analyzing the "Sleeping Giants" Activism Model in Brazil
B\'arbara Gomes Ribeiro, Manoel Horta Ribeiro, Virg\'ilio Almeida,, Wagner Meira Jr

TL;DR
This paper provides a detailed quantitative analysis of the Sleeping Giants activism campaigns in Brazil, showing high success in company responses but limited impact on online attention and engagement with targeted media outlets.
Contribution
It offers the first comprehensive quantitative characterization of the Sleeping Giants activism model in Brazil, analyzing campaign effectiveness and online impact using digital traces and classifiers.
Findings
83.85% of targeted companies responded positively
No significant change in online attention post-campaign
User interactions with companies were only transient
Abstract
In 2020, amidst the COVID pandemic and a polarized political climate, the Sleeping Giants online activist movement gained traction in Brazil. Its rationale was simple: to curb the spread of misinformation by harming the advertising revenue of sources that produce this type of content. Like its international counterparts, Sleeping Giants Brasil (SGB) campaigned against media outlets using Twitter to ask companies to remove ads from the targeted outlets. This work presents a thorough quantitative characterization of this activism model, analyzing the three campaigns carried out by SGB between May and September 2020. To do so, we use digital traces from both Twitter and Google Trends, toxicity and sentiment classifiers trained for the Portuguese language, and an annotated corpus of SGB's tweets. Our key findings were threefold. First, we found that SGB's requests to companies were largely…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMisinformation and Its Impacts · Social Media and Politics · Hate Speech and Cyberbullying Detection
