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- ItemCheck square at CheckThat! 2020: Claim Detection in Social Media via Fusion of Transformer and Syntactic Features(Aachen, Germany : RWTH Aachen, 2020) Cheema, Gullasl S.; Hakimov, Sherzod; Ewerth, Ralph; Cappellato, Linda; Eickhoff, Carsten; Ferro, Nicola; Névéol, AurélieIn this digital age of news consumption, a news reader has the ability to react, express and share opinions with others in a highly interactive and fast manner. As a consequence, fake news has made its way into our daily life because of very limited capacity to verify news on the Internet by large companies as well as individuals. In this paper, we focus on solving two problems which are part of the fact-checking ecosystem that can help to automate fact-checking of claims in an ever increasing stream of content on social media. For the first prob-lem, claim check-worthiness prediction, we explore the fusion of syntac-tic features and deep transformer Bidirectional Encoder Representations from Transformers (BERT) embeddings, to classify check-worthiness of a tweet, i.e. whether it includes a claim or not. We conduct a detailed feature analysis and present our best performing models for English and Arabic tweets. For the second problem, claim retrieval, we explore the pre-trained embeddings from a Siamese network transformer model (sentence-transformers) specifically trained for semantic textual similar-ity, and perform KD-search to retrieve verified claims with respect to a query tweet.
- ItemOn the Role of Images for Analyzing Claims in Social Media(Aachen, Germany : RWTH Aachen, 2021) Cheema, Gullal S.; Hakimov, Sherzod; Müller-Budack, Eric; Ewerth, RalphFake news is a severe problem in social media. In this paper, we present an empirical study on visual, textual, and multimodal models for the tasks of claim, claim check-worthiness, and conspiracy detection, all of which are related to fake news detection. Recent work suggests that images are more influential than text and often appear alongside fake text. To this end, several multimodal models have been proposed in recent years that use images along with text to detect fake news on social media sites like Twitter. However, the role of images is not well understood for claim detection, specifically using transformer-based textual and multimodal models. We investigate state-of-the-art models for images, text (Transformer-based), and multimodal information for four different datasets across two languages to understand the role of images in the task of claim and conspiracy detection.
- ItemThe role of blogs and news sites in science communication during the COVID-19 pandemic(Lausanne : Frontiers Media, 2022) Fraumann, Grischa; Colavizza, GiovanniWe present a brief review of literature related to blogs and news sites; our focus is on publications related to COVID-19. We primarily focus on the role of blogs and news sites in disseminating research on COVID-19 to the wider public, that is knowledge transfer channels. The review is for researchers and practitioners in scholarly communication and social media studies of science who would like to find out more about the role of blogs and news sites during the COVID-19 pandemic. From our review, we see that blogs and news sites are widely used as scholarly communication channels and are closely related to each other. That is, the same research might be reported in blogs and news sites at the same time. They both play a particular role in higher education and research systems, due to the increasing blogging and science communication activity of researchers and higher education institutions (HEIs). We conclude that these two media types have been playing an important role for a long time in disseminating research, which even increased during the COVID-19 pandemic. This can be verified, for example, through knowledge graphs on COVID-19 publications that contain a significant amount of scientific publications mentioned in blogs and news sites.
- ItemResorting to Context-Aware Background Knowledge for Unveiling Semantically Related Social Media Posts(New York, NY : IEEE, 2022) Sakor, Ahmad; Singh, Kuldeep; Vidal, Maria-EstherSocial media networks have become a prime source for sharing news, opinions, and research accomplishments in various domains, and hundreds of millions of posts are announced daily. Given this wealth of information in social media, finding related announcements has become a relevant task, particularly in trending news (e.g., COVID-19 or lung cancer). To facilitate the search of connected posts, social networks enable users to annotate their posts, e.g., with hashtags in tweets. Albeit effective, an annotation-based search is limited because results will only include the posts that share the same annotations. This paper focuses on retrieving context-related posts based on a specific topic, and presents PINYON, a knowledge-driven framework, that retrieves associated posts effectively. PINYON implements a two-fold pipeline. First, it encodes, in a graph, a CORPUS of posts and an input post; posts are annotated with entities for existing knowledge graphs and connected based on the similarity of their entities. In a decoding phase, the encoded graph is used to discover communities of related posts. We cast this problem into the Vertex Coloring Problem, where communities of similar posts include the posts annotated with entities colored with the same colors. Built on results reported in the graph theory, PINYON implements the decoding phase guided by a heuristic-based method that determines relatedness among posts based on contextual knowledge, and efficiently groups the most similar posts in the same communities. PINYON is empirically evaluated on various datasets and compared with state-of-the-art implementations of the decoding phase. The quality of the generated communities is also analyzed based on multiple metrics. The observed outcomes indicate that PINYON accurately identifies semantically related posts in different contexts. Moreover, the reported results put in perspective the impact of known properties about the optimality of existing heuristics for vertex graph coloring and their implications on PINYON scalability.
- ItemLernort Bibliothek in Pandemie-Zeiten : zeitgemäßes Lernen und digitale Bildungsangebote in Öffentlichen Bibliotheken(Reutlingen : Berufsverband Information Bibliothek, 2021) Fahrenkrog, GabrieleMit den Veränderungen durch die Digitalisierung im Bildungsbereich und insbesondere mit den Bedingungen, die durch COVID-19 und den damit verbundenen Schul- und Bibliotheksschließungen einhergingen, veränderten sich auch die Anforderungen an den Lernort Bibliothek. Als Ort, um allein oder gemeinsam in der Gruppe zu lernen, wurden Bibliotheken im Laufe der Jahre immer beliebter. Was aber bleibt davon, wenn die Bibliothek pandemiebedingt geschlossen bleiben muss und wie können sich Bibliotheken ausrichten, um bei möglichen erneuten Schließungen von Schulen und Bibliotheken trotzdem geeignete Angebote zu machen und Lernort zu bleiben?
- ItemThe impact of the covid-19 pandemic on theworking conditions, employment, career development and well-being of refugee researchers(Basel : MDPI, 2021) Tzoraki, Ourania; Dimitrova, Svetlana; Barzakov, Marin; Yaseen, Saad; Gavalas, Vasilis; Harb, Hani; Haidari, Abas; Cahill, Brian P.; Ćulibrk, Alexandra; Nikolarea, Ekaterini; Andrianopulu, Eleni; Trajanovic, MiroslavThe ongoing ‘refugee crisis’ of the past years has led to the migration of refugee researchers (RRs) to European countries. Due to the COVID-19 pandemic, RRs often had to work from home and/or to continue their social, cultural and economic integration process under new conditions. An online survey carried out to explore the impact of the pandemic on the refugee researchers showed that RRs found it difficult to adapt their everyday working life to the ‘home’ setting. The majority have had neither a suitable work environment at home nor the appropriate technology. Although they stated that they are rather pleased with the measures taken by the public authorities, they expressed concern about their vulnerability due to their precarious contracts and the bureaucratic asylum procedures, as the pandemic has had a negative impact on these major issues. The majority of RRs working in academia seem not to have been affected at all as far as their income is concerned, while the majority of those employed in other sectors became unemployed during the pandemic (58%). Recommendations are provided to the public authorities and policy makers to assist RRs to mitigate the consequences of the pandemic on their life.