Trustworthiness of news stories in media

In my previous project, DISINFTRUST, at Cardiff University, I developed a natural language processing (NLP) model to assess the trustworthiness of news articles on online platforms. Through online surveys, I identified factors that influence people's trust levels in online news articles. Based on these insights, I created datasets to train NLP models and tested them. Consequently, I developed an NLP model that accurately identifies untrustworthy news articles.

Biases, Prior Experience, and Probability

For my PhD project at University College London (UCL), I investigated how various factors interact and collectively affect learning probabilistic morpho-phonological patterns. 

From artificial language learning experiments, I found that learners' bias against homophony caused learners to be poorer at learning a neutralizing alternation compared to a non-neutralizing alternation. 

However, when learners were frequently exposed to neutralizations in their native language, they were equally good at learning both alternations.

I implement these findings in a MaxEnt model by discounting the homophonous words in the data.