ABSTRACT: The increasing digitization of our society radically changes how we use digital media, exchange information, and make decisions. This development also changes how social scientists collect data on human behavior and experience in the field. One new form of data comes from in-vivo high-frequency mobile sensing via smartphones. Mobile sensing allows for the investigation of formerly intangible psychological constructs with objective data. In particular mobile sensing enables fine-grained, longitudinal data collections in the wild and at large scale. The additional combination of mobile sensing with state of the art machine learning methods, provides a perspective for the direct prediction of psychological traits and behavioral outcomes from these data. In this talk I will give an overview on my work combining machine learning with mobile sensing and discuss the opportunities and limitations of this approach. Consequently, I will provide an outlook perspective on where the routine use of mobile psychological sensing could take research and society alike.
ABSTRACT: The interest around automated fact-checking has increased as misinformation has become a major problem online. A typical pipeline for an automated fact-checking system consists of four steps: (1) detecting check-worthy claims, (2) retrieving relevant documents, (3) selecting most relevant snippets for the claim and (4) predicting the veracity of the claim. In this talk, I will talk about the use of state-of-the-art deep neural networks such as LSTMs and Transformer architectures for these steps. Specifically, how deep hierarchical attention networks can be used for predicting the veracity of the claims and how to use the attention weights to extract the evidence for the claims. In addition, I will also talk about how to do check-worthy claim detection using Transformer models. Using several benchmarks from political debates and manual fact checking websites such as Politifact and Snopes, we show that these models outperform strong baselines. I will also summarize the state-of-the-art research within the areas of automated fact-checking and conclude with a set of challenges and problems remaining in this area.
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Please join the Center for Data Science (CEDAS, UiB) and MediaFutures for an invited talk by Himan Abdollahpouri from the Northwestern University, USA, about the topic of popularity bias in recommender systems. Welcome to all! TITLE: User-centered Investigation of Popularity Bias in Recommender Systems. WHEN: 6 May 2021, 14:15-15:00 WHERE: https://uib.zoom.us/j/63657771765?pwd=anZlNkVPdkxoQ0FmZit5WDJ0R3FkQT09 ABSTRACT: Recommendation and ranking […]
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Details to be announced.