Social media and online environments have become an integral part of society, allowing for self-expression, information sharing, and discussions online. However, these platforms are also used to express hate and threats of violence. Violent threats online lead to negative consequences, such as an unsafe online environment, self-censorship, and endangering democracy. Manually detecting and moderating threats online is challenging due to the vast amounts of data uploaded daily. Scholars have called for efficient tools based on machine learning to tackle this problem. Another challenge is that few threat-focused datasets and models exist, especially for low-resource languages such as Swedish, making identifying and detecting threats challenging. Therefore, this study aims to develop a practical and effective tool to automatically detect and identify online threats in Swedish. A tailored Swedish threat dataset will be generated to fine-tune KBLab’s Swedish BERT model. The research question that guides this project is “How effective is a fine-tuned BERT model in classifying texts as threatening or non-threatening in Swedish online environments?”. To the authors’ knowledge, no existing model can detect threats in Swedish. This study uses design science research to develop the artifact and evaluates the artifact’s performance using experiments. The dataset will be generated during the design and development by manually annotating translated English, synthetic, and authentic Swedish data. The BERT model will be fine-tuned using hyperparameters from previous research. The generated dataset comprised 6,040 posts split into 39% threats and 61% non-threats. The model, NOVA, achieved good performance on the test set and in the wild - successfully differentiating threats from non-threats. NOVA achieved almost perfect recall but a lower precision - indicating room for improvement. NOVA might be too lenient when classifying threats, which could be attributed to the complexity and ambiguity of threats and the relatively small dataset. Nevertheless, NOVA can be used as a filter to identify threatening posts online among vast amounts of data.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:su-219703 |
| Date | January 2023 |
| Creators | Lindén, Kevin, Moshfegh, Arvin |
| Publisher | Stockholms universitet, Institutionen för data- och systemvetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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