Owen Conlan

Dipto Barman, Owen Conlan

The growth of online Digital/social media has allowed a variety of ideas and opinions to coexist. Social Media has appealed users due to the ease of fast dissemination of information at low cost and easy access. However, due to the growth in affordance of Digital platforms, users have become prone to consume disinformation, misinformation, propaganda, and conspiracy theories. In this paper, we wish to explore the links between the personality traits given by the Big Five Inventory and their susceptibility to disinformation. More speciDically, this study is attributed to capture the short- term as well as the long-term effects of disinformation and its effects on the Dive personality traits. Further, we expect to observe that different personalities traits have different shifts in opinion and different increase or decrease of uncertainty on an issue after consuming the disinformation. Based on the Dindings of this study, we would like to propose a personalized narrative-based change in behavior for different personality traits.

Esraa Ali, Annalina Caputo, Séamus Lawless et al.

Faceted Search Systems (FSS) have become one of the main search interfaces used in vertical search systems, offering users meaningful facets to refine their search query and narrow down the results quickly to find the intended search target. This work focuses on the problem of ranking type-based facets. In a structured information space, type-based facets (t-facets) indicate the category to which each object belongs. When they belong to a large multi-level taxonomy, it is desirable to rank them separately before ranking other facet groups. This helps the searcher in filtering the results according to their type first. This also makes it easier to rank the rest of the facets once the type of the intended search target is selected. Existing research employs the same ranking methods for different facet groups. In this research, we propose a two-step approach to personalize t-facet ranking. The first step assigns a relevance score to each individual leaf-node t-facet. The score is generated using probabilistic models and it reflects t-facet relevance to the query and the user profile. In the second step, this score is used to re-order and select the sub-tree to present to the user. We investigate the usefulness of the proposed method to a Point Of Interest (POI) suggestion task. Our evaluation aims at capturing the user effort required to fulfil her search needs by using the ranked facets. The proposed approach achieved better results than other existing personalized baselines.