The CSIS Hate Speech Dashboard tracks online hate speech trends in Indonesia. It uses a bespoke machine learning algorithm to collect Indonesian tweets on Twitter and identify whether they contain hate speech targeting one of five vulnerable minorities: Ahmadiyyas, Shi’as, Chinese Indonesian, Christians, and ethnic Papuans.
The dashboard is run by a team of researchers in the Centre for Strategic and International Studies (CSIS) Jakarta with support from the Asia Pacific Partnership for Atrocity Prevention (APPAP) and the Asia-Pacific Centre for the Responsibility to Protect (APR2P) Centre in the University of Queensland.
The Asia Pacific Partnership for Atrocity Prevention (APPAP) is an alliance of organizations in the Asia Pacific region that works towards the prevention of atrocity crimes and fostering commitment to the Responsibility to Protect (R2P) principle.
The alliance is composed of civil society, not-for-profit organizations, think tanks, and University schools and centers. The alliance meets regularly and works to prevent atrocity through groups that include the Gender and Atrocities Prevention Group and the Working Group on the Prevention of Hate Speech and Incitement. The APR2P Centre is the current secretariat of APPAP, and the alliance funds grassroots projects and education activities across Asia and the Pacific, including youth projects and the development of software applications.
Lina Alexandra, Alif Satria, Edbert Gani Suryahudaya, and Beltsazar Krisetya, “The National Hate Speech Dashboard,” CSIS Indonesia, (2021). https://hatespeech.csis.or.id
The CSIS Hate Speech Dashboard defines hate speech as “any tweet that uses phrases which legitimize hostile actions or ascribe negative qualities towards the identity of a vulnerable community.” For a tweet to be considered hate speech it must:
The CSIS Hate Speech Dashboard collects data on Twitter using the publicly available SNScraper
The CSIS Hate Speech Dashboard identifies which tweets are hate speech by using an independently developed hate speech identification algorithm. This algorithm is developed in a four-step process: