Overview Research on knowledge graph construction (KGC) has recently shown great promise also thanks to the adoption of large language models (LLM) for the automatic extraction of structured information from raw text.
Overview This study examines whether the psycholinguistic and demographic characteristics of authors of online texts are correlated with the way harmful language, such as toxicity and hate speech, is judged. We apply artificial intelligence models to two harmful language datasets, Jigsaw’s Special Rater Pool dataset and the Measuring Hate Speech dataset, to generate probabilities for different text aspects, namely inferring demographic information of the author behind the suspicious text in terms of age and gender, as well as the expressed emotions, emotionality, sentiment and communication style.
Overview Electronic mail (email) is one of the most popular communication media for direct and private communication. Being typically a free service and anonymity-friendly, massive spam email campaigns are common. Nowadays, spam email encompasses scam, phishing, malware distribution, and various other cybersecurity threats.
This paper was led by Arianna Muti as part of her PhD.
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