Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation

With the uptake of algorithmic personalization in the news domain, news organizations increasingly trust automated systems with previously considered editorial responsibilities, e.g., prioritizing news to readers. In this paper we study an automated news recommender system in the context of a news organization's editorial values. We conduct and present two online studies with a news recommender system, which span one and a half months and involve over 1,200 users. In our first study we explore how our news recommender steers reading behavior in the context of editorial values such as serendipity, dynamism, diversity, and coverage. Next, we present an intervention study where we extend our news recommender to steer our readers to more dynamic reading behavior. We find that (i) our recommender system yields more diverse reading behavior and yields a higher coverage of articles compared to non-personalized editorial rankings, and (ii) we can successfully incorporate dynamism in our recommender system as a re-ranking method, effectively steering our readers to more dynamic articles without hurting our recommender system's accuracy.Comment: To appear in UMAP 202

Early Detection of Cyberbullying on Social Media Networks

[Abstract] Cyberbullying is an important issue for our society and has a major negative effect on the victims, that can be highly damaging due to the frequency and high propagation provided by Information Technologies. Therefore, the early detection of cyberbullying in social networks becomes crucial to mitigate the impact on the victims. In this article, we aim to explore different approaches that take into account the time in the detection of cyberbullying in social networks. We follow a supervised learning method with two different specific early detection models, named threshold and dual. The former follows a more simple approach, while the latter requires two machine learning models. To the best of our knowledge, this is the first attempt to investigate the early detection of cyberbullying. We propose two groups of features and two early detection methods, specifically designed for this problem. We conduct an extensive evaluation using a real world dataset, following a time-aware evaluation that penalizes late detections. Our results show how we can improve baseline detection models up to 42%.This research was supported by the Ministry of Economy and Competitiveness of Spain and FEDER funds of the European Union (Project PID2019-111388GB-I00) and by the Centro de Investigación de Galicia “CITIC”, funded by Xunta de Galicia (Galicia, Spain) and the European Union (European Regional Development Fund — Galicia 2014–2020 Program) , by grant ED431G 2019/01Xunta de Galicia; ED431G 2019/0

Ontology model for zakat hadith knowledge based on causal relationship, semantic relatedness and suggestion extraction

Hadith is the second most important source used by all Muslims. However, semantic ambiguity in the hadith raises issues such as misinterpretation, misunderstanding, and misjudgement of the hadith’s content. How to tackle the semantic ambiguity will be focused on this research (RQ). The Zakat hadith data should be expressed semantically by changing the surface-level semantics to a deeper sense of the intended meaning. This can be achieved using an ontology model covering three main aspects (i.e., semantic relationship extraction, causal relationship representation, and suggestion extraction). This study aims to resolve the semantic ambiguity in hadith, particularly in the Zakat topic by proposing a semantic approach to resolve semantic ambiguity, representing causal relationships in the Zakat ontology model, proposing methods to extract suggestion polarity in hadith, and building the ontology model for Zakat topic. The selection of the Zakat topic is based on the survey findings that respondents still lack knowledge and understanding of the Zakat process. Four hadith book types (i.e., Sahih Bukhari, Sahih Muslim, Sunan Abu Dawud, and Sunan Ibn Majah) that was covering 334 concept words and 247 hadiths were analysed. The Zakat ontology modelling cover three phases which are Preliminary study, source selection and data collection, data pre-processing and analysis, and development and evaluation of ontology models. Domain experts in language, Zakat hadith, and ontology have evaluated the Zakat ontology and identified that 85% of Zakat concept was defined correctly. The Ontology Usability Scale was used to evaluate the final ontology model. An expert in ontology development evaluated the ontology that was developed in Protégé OWL, while 80 respondents evaluated the ontology concepts developed in PHP systems. The evaluation results show that the Zakat ontology has resolved the issue of ambiguity and misunderstanding of the Zakat process in the Zakat hadith. The Zakat ontology model also allows practitioners in Natural language processing (NLP), hadith, and ontology to extract Zakat hadith based on the representation of a reusable formal model, as well as causal relationships and the suggestion polarity of the Zakat hadith

Soundscapes of the Urban Past: Staged Sound as Mediated Cultural Heritage

We cannot simply listen to our urban past. Yet we encounter a rich cultural heritage of city sounds presented in text, radio and film. How can such "staged sounds" express the changing identities of cities? This volume presents a collection of studies on the staging of Amsterdam, Berlin and London soundscapes in historical documents, radio plays and films, and offers insights into themes such as film sound theory and museum audio guides. In doing so, this book puts contemporary controversies on urban sound in historical perspective, and contextualises iconic presentations of cities. It addresses academics, students, and museum workers alike

A social network of crime : A review of the use of social networks for crime and the detection of crime

Social media is used to commit and detect crimes. With automated methods, it is possible to scale both crime and detection of crime to a large number of people. The ability of criminals to reach large numbers of people has made this area subject to frequent study, and consequently, there have been several surveys that have reviewed specific crimes committed on social platforms. Until now, there has not been a review article that considers all types of crimes on social media, their similarity as well as their detection. The demonstration of similarity between crimes and their detection methods allows for the transfer of techniques and data between domains. This survey, therefore, seeks to document the crimes that have been committed on social media, and demonstrate their similarity through a taxonomy of crimes. Also, this survey documents publicly available datasets. Finally, this survey provides suggestions for further research in this field

Investigating user experience and bias mitigation of the multi-modal retrieval of historical data

Decolonisation has raised the discussion of technology having the responsibility of presenting multiple perspectives to users. This is specifically relevant to African precolonial heritage artefact data, where the data contains the bias of the curators of the artefacts and there are primary concerns surrounding the social responsibility of these systems. Historians have argued that common information retrieval algorithms may further bias results presented to users. While research for mitigating bias in information retrieval is steered in the direction of artificial intelligence and automation, an often-neglected approach is that of user-control. User-control has proven to be beneficial in other research areas and is strongly aligned with the core principles of decolonisation. Thus, the effects on user experience, bias mitigation, and retrieval effectiveness from the addition of user-control and algorithmic variation to a multimodal information retrieval system containing precolonial African heritage data was investigated in this study. This was done by conducting two experiments: 1) an experiment to provide a baseline offline evaluation of various algorithms for text and image retrieval and 2) an experiment to investigate the user experience with a retrieval system that allowed them to compare algorithms. In the first experiment, the differences in retrieval effectiveness between colour-based pre-processing algorithms, shape-based preprocessing algorithms, and pre-processing algorithms based on a combination of colour- and shape-detection, was explored. The differences in retrieval effectiveness between stemming, stopword removal and synonym query expansion was also evaluated for text retrieval. In the second experiment, the manner in which users experience bias in the context of common information retrieval algorithms for both the textual and image data that are available in typical historical archives was explored. Users were presented with the results generated by multiple algorithmic variations, in a variety of different result formats, and using a variety of different search methods, affording them the opportunity to decide what they deem provides them with a more relevant set of results. The results of the study show that algorithmic variation can lead to significantly improved retrieval performance with respect to image-based retrieval. The results also show that users potentially prefer shape-based image algorithms rather than colour-based image algorithms, and, that shape-based image algorithms can lead to significantly improved retrieval of historical data. The results also show that users have justifiable preferences for multimodal query and result formats to improve user experience and that users believe they can control bias using algorithmic variatio

Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

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