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Visar resultat 1 - 5 av 32 uppsatser som matchar ovanstående sökkriterier.

  1. 1. Mitigating Unintended Bias in Toxic Comment Detection using Entropy-based Attention Regularization

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Fabio Camerota; [2023]
    Nyckelord :XLNet; BERT; Toxic Comment Classification; Entropy-based Attention Regularization; XLNet; BERT; Toxisk Kommentar Klassificering; Entropibaserad uppmärksamhetsreglering;

    Sammanfattning : The proliferation of hate speech is a growing challenge for social media platforms, as toxic online comments can have dangerous consequences also in real life. There is a need for tools that can automatically and reliably detect hateful comments, and deep learning models have proven effective in solving this issue. LÄS MER

  3. 2. Hatet som tystar : En definition av näthat

    Kandidat-uppsats, Enskilda Högskolan Stockholm/Avdelningen för mänskliga rättigheter och demokrati

    Författare :Erla Lorentzon; [2023]
    Nyckelord :Det demokratiska samtalet; medie- och informationskunnighet; motståndskraft; yttrandefrihet; självcensur; informationspåverkan; näthat;

    Sammanfattning : .... LÄS MER

  4. 3. A Hybrid Approach to Hate Speech Detection

    Master-uppsats, Umeå universitet/Institutionen för datavetenskap

    Författare :Simon Rickardsson; [2023]
    Nyckelord :;

    Sammanfattning : An interesting question is to what extent can background knowledge help in the context of text classification. To address this in more detail, can a traditional rulebased classifier help boost the accuracy of learned models? We explore this here for detecting hate speech and offensive language in online text. LÄS MER

  5. 4. Can Hatescan Detect Antisemitic Hate Speech

    Kandidat-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

    Författare :Olle Nyrén; [2023]
    Nyckelord :Artificial intelligence; hate speech detection; hate speech; antisemitism;

    Sammanfattning : This thesis focuses on how well Hatescan, a hate speech detector built on the same Natural Language Processing and AI algorithms used in most online hate speech detectors, can detect different categories of antisemitism as well as whether or not it is worse at detecting implicit antisemitism than explicit antisemitism. The ability of hate speech detectors to detect antisemitic hate speech is a pressing issue. LÄS MER

  7. 5. Exploring toxic lexicon similarity methods with the DRG framework on the toxic style transfer task

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Martin Iglesias; [2023]
    Nyckelord :Detoxifcation; Text style transfer; Deep learning; Transformers; Linguistics; Natural Language Processing; Hate speech; Text style-conditional generation; Large language model; Avgiftning; Överföring av textstil; Djupinlärning; Transformatorer; Lingvistik; Naturlig språkbehandling; Hatprat; Textstil - villkorlig generering; Stor språkmodell;

    Sammanfattning : The topic of this thesis is the detoxification of language in social networks with a particular focus on style transfer techniques that combine deep learning and linguistic resources. In today’s digital landscape, social networks are rife with communication that can often be toxic, either intentionally or unintentionally. LÄS MER