HumanISE

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This service focuses its action on information systems applied to the sectors of autarchies, industry, commerce, health, telecommunications and central and regional administration.

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Browsing HumanISE by Author "5448"
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    Can user and task characteristics be used as predictors of success in health information retrieval sessions?
    ( 2018) Carla Lopes ; Sérgio Nunes ; Oroszlanyova,M ; Cristina Ribeiro ; 215 ; 5448 ; 6205
    Introduction. The concept and study of relevance has been a central subject in information science. Although research in information retrieval has been focused on topical relevance, other kinds of relevance are also important and justify further study. Motivational relevance is typically inferred by criteria such as user satisfaction and success. Method. Using an existing dataset composed by an annotated set of health Web documents assessed for relevance and comprehension by a group of users, we build a multivariate prediction model for the motivational relevance of search sessions. Analysis. The analysis was based on lasso variable selection, followed by model selection using multiple logistic regression. Results. We have built two regression models; the full model, which considers all variables of the dataset, has a lower estimated prediction error than the reduced model, which contains the statistically-significant variables from the full model. The higher values of evaluation metrics, including accuracy, specificity and sensitivity in the full model support this finding. The full model has an accuracy of 91.94%, and is better at predicting motivational relevance. Conclusions. Our findings suggest features that can be considered by search engines to estimate motivational relevance, to be used in addition to topical relevance. Among these features, a high level of success in Web search and in health information search on social networks and chats are some of the most influencing user features. This shows that users with higher computer literacy might feel more satisfied and successful after completing the search tasks. In terms of task features, the results suggest that users with clearer goals feel more successful. Moreover, results show that users would benefit from the help of the system in clarifying the retrieved documents.
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    FEUP at TREC 2017 OpenSearch Track Graph-Based Models for Entity-Oriented
    ( 2017) José Luís Devezas ; Carla Lopes ; Sérgio Nunes ; 5585 ; 6205 ; 5448
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    Merging Datasets for Hate Speech Classification in Italian
    ( 2018) Sérgio Nunes ; Fortuna,P ; Bonavita,I ; 5448
    This paper presents an approach to the shared task HaSpeeDe within Evalita 2018. We followed a standard machine learning procedure with training, validation, and testing phases. We considered word embedding as features and deep learning for classification. We tested the effect of merging two datasets in the classification of messages from Facebook and Twitter. We concluded that using data for training and testing from the same social network was a requirement to achieve a good performance. Moreover, adding data from a different social network allowed to improve the results, indicating that more generalized models can be an advantage.
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    Preface
    ( 2018) Ricardo Campos ; Sérgio Nunes ; Jatowt,A ; Alípio Jorge ; 4981 ; 5782 ; 5448
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    Social Media and Information Consumption Diversity
    ( 2018) Sérgio Nunes ; José Luís Devezas ; 5448 ; 5585
    Social media platforms are having a profound impact on the so-called information ecosystem, specifically on how information is produced, distributed and consumed. Social media in particular has contributed to the rise of user generated content and consequently to a greater diversity in online content. On the other hand, social media networks, such as Twitter or Facebook, have become information management tools that allow users to setup and configure information sources to their particular interests. A Twitter user can handpick the sources he wishes to follow, thus creating a custom information channel. However, this opportunity to create personalized information channels effectively results in different consumption profiles? Is the information consumed by users through social media networks distinct from the information consumed though traditional mainstream media? In this work, we set out to investigate this question using Twitter as a case study. We prepare two samples of users, one based on a uniform random selection of user IDs, and another one based on a selection of mainstream media followers. We analyze the home timelines of the users in each sample, focusing on characterizing information consumption habits. We find that information consumption volume is higher, while diversity is consistently lower, for mainstream media followers when compared to random users. When analyzing daily behavior, however, the samples slightly approximate, while clearly maintaining a lower diversity for mainstream media followers and a higher diversity for random users. Copyright © 2018 for the individual papers by the papers’ authors.
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    A Survey on Automatic Detection of Hate Speech in Text
    ( 2018) Sérgio Nunes ; Fortuna,P ; 5448
    The scientific study of hate speech, from a computer science point of view, is recent. This survey organizes and describes the current state of the field, providing a structured overview of previous approaches, including core algorithms, methods, and main features used. This work also discusses the complexity of the concept of hate speech, defined in many platforms and contexts, and provides a unifying definition. This area has an unquestionable potential for societal impact, particularly in online communities and digital media platforms. The development and systematization of shared resources, such as guidelines, annotated datasets in multiple languages, and algorithms, is a crucial step in advancing the automatic detection of hate speech. © 2018 ACM.