Exploring Roles of Emotion in Fake News Detection

Detecting fake news is becoming widely acknowledged as a critical activity with significant implications for social impact. As fake news tends to evoke high-activating emotions from audiences, the role of emotions in identifying fake news is still under-explored. Existing research made efforts in examining effective representations of emotions conveyed in the news content to help discern the veracity of the news. However, the aroused emotions from the audience are usually ignored. This paper first demonstrates effective representations of emotions within both news content and users’ comments. Furthermore, we propose an emotion-aware fake news detection framework that seamlessly incorporates emotion features to enhance the accuracy of identifying fake news. Future work will include thorough experiments to prove that the proposed framework with the emotions expressed in news and users’ comments improves fake news detection performance

Fake News Detection: Covid-19 Perspective

The development of social media has con- tributed to a remarkable rise in the spread of fake news. Today people rely more on online news outlets. The chance of receiving fake news on an online platform is high. As we went through a pandemic and the Covid-19 was the most absorbing topic of 2020, much news on Covid-19 was published every day in traditional media and social media. Among that news, some are fake. In this work, we have collected a new dataset for detecting fake news from traditional media on Covid-19. We have gathered more than 3000 pieces of news from traditional media out of the 170 are fake ones that were collected from fact-checking sites. Then we have tested the existing four classification algorithms with our dataset using Count Vectorizer and TF-IDF. We have merged 170 fake news with four scales of true news and analyzed the outcome

Bridging the Gap

Effective organization and retrieval of news content are heavily reliant on accurate news classification. While the mountainous research has been conducted in resourceful languages like English and Chinese, the researches on under-resourced languages like the Kurdish language are severely lacking. To address this challenge, we introduce a hybrid approach called RFO-CNN in this paper. The proposed method combines an improved version of red fox optimization algorithm (RFO) and convolutional neural network (CNN) for finetuning CNN’s parameters. Our model’s efficacy was tested on two widely used Kurdish news datasets, KNDH and KDC-4007, both of which contain news articles classified into various categories. We compared the performance of RFO-CNN to other cutting-edge deep learning models such as bidirectional long short-term memory networks and bidirectional encoder representations from transformers (BERT) transformers, as well as classical machine learning approaches such as multinomial naive bayes, support vector machine, and K-nearest neighbors. We trained and tested our datasets using four different scenarios: 60:40, 70:30, 80:20, and 90:10. Our experimental results demonstrate the superiority of the RFO-CNN model across all scenarios, outperforming the benchmark BERT model and other machine learning models in terms of accuracy and F1-score

Social media disinformation in the pre-electoral period in Portugal

Since the North American presidential election of 2016, the role of social media on the propagation of misleading news and its instrumentalization by partisan groups has raised concerns. In this article we analyse the contents of 47 Facebook pages and 39 Facebook groups prior to the Portuguese parliamentary election of 6th of October of 2019 to track disinformation. Groups and pages to monitor were selected through a process that combined the number of fans or members, the proportion of political content, and the number of posts per week. We concluded that disinformative content was prevalent in the pages and groups monitored, that several political actors had a relevant influence on the debate and that most disinformation stemmed from the spinning of both mainstream and non-mainstream news to serve a political purpose

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

Peer reviewedPostprin

How the AIS can Improve its Contributions to the UN’s Sustainability Development Goals: Towards A Framework for Scaling Collaborations and Evaluating Impact

In June, 2019, the Association for Information Systems (AIS) adopted a new approach to addressing global sustainability issues by establishing the AIS Sustainability Task Force (AIS STF). This initiative focuses on building on the outcomes from the United Nations (UN) Millennium Development Goals (MDG, 2000-2015) and applying them to address the challenges associated with the UN Sustainable Development Goals (SDG, 2016-2030). In this paper, we review the challenges and outcomes from the UN sustainability programs with their potential relevance to IS in general and the AIS in particular to inform and assist increased efforts to achieve the global sustainability goals. The initial event, the AIS Sustainability Summit held at ICIS 2019, provided a forum for AIS groups and communities to share their current interests, plans, activities, and experiences relevant to the MDG and SDG. The event primarily focused on facilitating opportunities to scale the AIS’s sustainability activities through multi-disciplinary collaboration across the AIS and its communities. Members from four AIS special interest groups and the STF’s Education Workgroup presented exemplary projects at the summit that demonstrated how one can apply applied IS and research capabilities to address sustainability challenges. The sustainability summit’s also explored opportunities to achieve positive impact in addressing the SDG’s global challenges through applying AIS members’ knowledge, skills, and capabilities in relevant ways in collaboration with suitable organizations outside the AIS. Potential organizations include business, government, societal groups, and UN bodies. We presented and discussed the AIS STF’s aims, plans, outcomes, and impact. By analyzing details and options for cross-organizational collaboration, the representatives of organizations at the sustainability summit developed a proposed framework for scaling contributions and evaluating impact. Finally, they drew conclusions about the proposed activities, approaches, and framework for the AIS to improve the scope and scale of its contributions in addressing the SDG. Critically, the AIS needs to ensure that its proposed activities, contributions, and impact are examined by an internationally recognized independent process. We propose a model for the AIS to realize this requirement for evaluation in 2021

Viralizing the truth: predictive factors of fact-checkers’ engagement on TikTok

Disinformation is one of the great challenges threatening the health of the public space and democratic systems, which must be based on truth to facilitate decision-making by citizens. For this reason, the fight against fake news has attracted attention from professionals and researchers during the last five years. In the journalistic field, numerous fact-checking outlets have been established. They use the media at their disposal and, above all, social networks to make verified content visible. TikTok, an emerging social video platform (very popular among the youth population), is the latest tool to be explored by fact-checkers to make the truth public. In this context, this paper aims to (1) determine the characteristics of fact-checkers’ videos that drive their engagement on TikTok and (2) identify the factors that predict the number of likes, comments, views, times shared, and engagement rate of such content. All the videos published on this platform by the Spanish fact-checkers Maldito bulo and Newtral (n = 320) during the first 26 months of activity of both outlets were analyzed. Bivariate correlational studies, multiple linear regression, and binary logistic regression tests were applied. The type of content (verification versus explanation) has a greater impact on engagement than the topics of the videos. Verifications multiply the probability of a content getting an above-average number of likes, by 2.42. If the video is hosted by a woman, it doubles its chances of getting an above-average number of shares. Our results provide other valuable data that can help fact-checkers make their content more easily spreadable