1,721,061 research outputs found
Enhancing media literacy : The effectiveness of (Human) annotations and bias visualizations on bias detection
No full textMarking biased texts effectively increases media bias awareness, but its sustainability across new topics and unmarked news remains unclear, and the role of AI-generated bias labels is untested. This study examines how news consumers learn to perceive media bias from human- and AI-generated labels and identify biased language through highlighting, neutral rephrasing, and political orientation cues. We conducted two experiments with a teaching phase exposing them to various bias-labeling conditions and a testing phase evaluating their ability to classify biased sentences and detect biased text in unlabeled news on new topics.
We find that, compared to the control group, both human- and AI-generated sentential bias labels significantly improve bias classification (p < .001), though human labels are more effective (d = 0.42 vs. d = 0.23). Additionally, among all teaching interventions, participants best detect biased sentences when taught with biased sentence or phrase labels (p < .001), while politicized phrase labels reduce accuracy. The effectiveness of different media literacy interventions remains independent of political ideology, but conservative participants are generally less accurate (p = .011), suggesting an interaction between political inclinations and bias detection.
Our research provides a novel experimental framework into assessing the generalizability of media bias awareness and offer practical implications for designing bias indicators in news-reading platforms and media literacy curricula.publishe
Zero-Shot Warning Generation for Misinformative Multimodal Content Detection
Get PDFThe widespread prevalence of misinformation poses serious societal concerns. Out-ofcontext misinformation, which involves authentic images paired with false text, is particularly insidious as it can easily deceive audiences. Existing detection methods primarily assess the consistency between images and text but often fall short in providing
sufficient explanations for their assessments. Such explanations are crucial for effectively
debunking misinformation. We have designed a model capable of detecting multimodal
misinformation through cross-modality consistency checks that surpasses the current
state-of-the-art models in terms of accuracy and training time. Furthermore, we have
developed a lightweight model that achieves accuracy better than that of the state-ofthe-art models while using only one-third the parameters. In addition, we devised a
dual-purpose zero-shot learning task for generating contextualized warnings, enabling
automatic debunking. The result is enhanced user comprehension and more informed
decision-making. Additionally, qualitative and human evaluation of the generated warnings shed light on both the limitations and potentialities of our proposed approach
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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