Prosjekt 2025

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It can be vital both for security and for mental health reasons to identify users at risk in social media. That can be users being at risk of being radicalised (as extremists, school shooters and other types of terrorists, etc.) or of being targeted by predators or extremist organisations, as well as users showing signs of mental health issues (depression, suicide, eating disorders, etc.). 
This thesis work would experiment with applying various machine learners to social media text. To fully utilise deep learning, a substantial amount of data will need to be gathered, either from previous research or specifically for the task. 

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The way we write texts give a lot of information about the background personalities of the authors: their age, gender, native language (if writing in a foreign language), if they're human or bots, and possibly their actual identity. This type of information can be used to, e.g., give fair indications of user profiles, to deduce if a text (or a part of it) has been plagiarised, or to uncover social media software misuse. The thesis could thus focus on tasks such as author profiling (what can we say about the author, e.g., their gender, age, if they're a human or a bot), author identification (did a specific author write this text?) and/or plagiarism detection (did somebody else than the author claiming the text actually write all or part of the text?)

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Large language models form the basis of almost all currently topical AI research, making it vital to identify and rectify different types of demographic biases in those models (be that bias based on gender or sexual identity, or on cultural, ethical or social background, etc.). This has triggered intense research on fair representation in language models, aiming both at building and using unbiased training and evaluation datasets, and at changing the actual learning algorithms themselves. There is still a lot of room for improvements though, both in identifying and quantifying bias, in developing dibiasing methods, and in defining bias as such.

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To be creative, we need to produce something which is new, meaningful and has some sort of value. Generative AI is able to support humans in creative processes, but to also itself be creative or to assess if an idea or a product is creative. A computational creativity project can investigate any creative field matching the interests and backgrounds of the student or students (language, design, music, art, mathematics, computer programming, etc.), and concentrate on one or several aspects of computational creativity, such as the production, understanding or evaluation of creativity, or on computer systems that support human creativity.

In particular, the project can investigate the transitions between different creative artforms, e.g., generating music or images based on textual input (as in Stable Diffusion models), generating music based on images or text, or generating text based on music or images.

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Properly identifying hatespeech is a pressing issue for social media sites as well as for smaller companies, clubs, and organisations that allow for user-generated content. Many such sites currently use slow, manual moderation, which mean that abusive posts will be left online for too long without appropriate action being taken or that content will be published with delay (which might be unacceptable to the users, e.g., in online chat rooms).

The project would look into previous efforts to identify hate speech and cyber bullying, as well as available flame-annotated datasets from chat rooms, online games, Wikipedia, X/Twitter, etc., and investigate various machine learning methods to identify such language.

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Several models can be used to find out how users’ social media networks, behaviour and language are related to their ethical practices and personalities, Such models include Schwartz’ values and ethics model and Goldberg's Big 5 model that defines personality traits such as openness, conscientiousness, extraversion, agreeableness and neuroticism. The thesis project would investigate applying such models to social media text and how the user personalties are reflected by the social networks that they participate in and develop.

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Social media posts often express sentiment (positive or negative emotions) towards a product, person, political party, etc. The project is aimed at the automatic classification of sentiment in texts on social media, tentatively addressing issues involving negation and/or figurative language, that is, language which intentionally conveys secondary or extended meanings (such as sarcasm, irony and metaphor). Figurative language creates a significant challenge for sentiment analysis systems, as direct approaches based on words and their lexical semantics often are inadequate in the face of indirect meanings. A subgoal of the project would then be to find a set of social media posts rich in figurative language, but the main goal would be to determine whether a user has expressed a positive or negative sentiment and possibly the degree to which this sentiment has been communicated.

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Languages change rapidly over time and language users adapt to different situations and setting. Studying how language and communicative processes evolves is a highly multi-disciplinary task involving machine and language learning as well as biological and cultural evolution. The aim of this project will be to use methods such as evolutionary algorithms or reinforcement learning to investigate the main dynamics in language evolution.

When trying to understand the origins of languages, we can compensate for the lack of empirical evidence by utilising evolutionary computational methods to create simulations of how language may have evolved over time, e.g., by creating "language games" to simulate communication between agents in a social setting. In general, simulations on language evolution tend to have relatively small and fixed population sizes, something this study could aim to change.

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