A1 Journal article (refereed)
The Datafication of Hate : Expectations and Challenges in Automated Hate Speech Monitoring (2020)
Laaksonen, S.-M., Haapoja, J., Kinnunen, T., Nelimarkka, M., & Pöyhtäri, R. (2020). The Datafication of Hate : Expectations and Challenges in Automated Hate Speech Monitoring. Frontiers in Big Data, 3, Article 3. https://doi.org/10.3389/fdata.2020.00003
JYU authors or editors
Publication details
All authors or editors: Laaksonen, Salla-Maaria; Haapoja, Jesse; Kinnunen, Teemu; Nelimarkka, Matti; Pöyhtäri, Reeta
Journal or series: Frontiers in Big Data
eISSN: 2624-909X
Publication year: 2020
Volume: 3
Article number: 3
Publisher: Frontiers Media
Publication country: Switzerland
Publication language: English
DOI: https://doi.org/10.3389/fdata.2020.00003
Publication open access: Openly available
Publication channel open access: Open Access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/67946
Publication is parallel published: https://aaltodoc.aalto.fi/handle/123456789/43087
Abstract
Hate speech has been identified as a pressing problem in society and several automated approaches have been designed to detect and prevent it. This paper reports and reflects upon an action research setting consisting of multi-organizational collaboration conducted during Finnish municipal elections in 2017, wherein a technical infrastructure was designed to automatically monitor candidates' social media updates for hate speech. The setting allowed us to engage in a 2-fold investigation. First, the collaboration offered a unique view for exploring how hate speech emerges as a technical problem. The project developed an adequately well-working algorithmic solution using supervised machine learning. We tested the performance of various feature extraction and machine learning methods and ended up using a combination of Bag-of-Words feature extraction with Support-Vector Machines. However, an automated approach required heavy simplification, such as using rudimentary scales for classifying hate speech and a reliance on word-based approaches, while in reality hate speech is a linguistic and social phenomenon with various tones and forms. Second, the action-research-oriented setting allowed us to observe affective responses, such as the hopes, dreams, and fears related to machine learning technology. Based on participatory observations, project artifacts and documents, interviews with project participants, and online reactions to the detection project, we identified participants' aspirations for effective automation as well as the level of neutrality and objectivity introduced by an algorithmic system. However, the participants expressed more critical views toward the system after the monitoring process. Our findings highlight how the powerful expectations related to technology can easily end up dominating a project dealing with a contested, topical social issue. We conclude by discussing the problematic aspects of datafying hate and suggesting some practical implications for hate speech recognition.
Keywords: hate speech; monitoring; text mining; machine learning; algorithms; data science; social media; politics
Free keywords: hate speech; machine learning; algorithmic system; data science; social media; politics
Contributing organizations
Ministry reporting: Yes
Reporting Year: 2020
JUFO rating: 1