Content-Based Unsupervised Fake News Detection on Ukraine-Russia War

The Ukrainian-Russian war has garnered significant attention worldwide, with fake news obstructing the formation of public opinion and disseminating false information. This scholarly paper explores the use of unsupervised learning methods and the Bidirectional Encoder Representations from Transformers (BERT) to detect fake news in news articles from various sources. BERT topic modeling is applied to cluster news articles by their respective topics, followed by summarization to measure the similarity scores. The hypothesis posits that topics with larger variances are more likely to contain fake news. The proposed method was evaluated using a dataset of approximately 1000 labeled news articles related to the Syrian war. The study found that while unsupervised content clustering with topic similarity was insufficient to detect fake news, it demonstrated the prevalence of fake news content and its potential for clustering by topic

Be all that you can be: Targeting library orientations to military cadets

At Texas A&M University, the Corps of Cadets is the largest and oldest student organization on campus. Each summer, nearly 1,000 freshman recruits participate in the Corps' Freshman Orientation Week (FOW), a military-style orientation. Because of their participation in FOW, cadets miss out on many other traditional freshman orientation opportunities. To reach this large and highly visible freshman cohort, the University Libraries adopted a military-inspired orientation approach that built on the unique nature of FOW. Librarians strategically used the bonds formed by cadets over the grueling week of FOW by gamifying the presentation, incorporating competition, and relying on the cadets' sense of camaraderie to create a fun and meaningful experience. This chapter presents a case study on how to target particular audiences in first-year orientations, specifically student cadets

Current Advances in Internet of Underground Things

The latest developments in Internet of Underground Things are covered in this chapter. First, the IOUT Architecture is discussed followed by the explanation of the challenges being faced in this paradigm. Moreover, a comprehensive coverage of the different IOUT components is presented that includes communications, sensing, and system integration with the cloud. An in-depth coverage of the applications of the IOUT in various disciplines is also surveyed. These applications include areas such as decision agriculture, pipeline monitoring, border control, and oil wells

Content-Based Unsupervised Fake News Detection on Ukraine-Russia War

The Ukrainian-Russian war has garnered significant attention worldwide, with fake news obstructing the formation of public opinion and disseminating false information. This scholarly paper explores the use of unsupervised learning methods and the Bidirectional Encoder Representations from Transformers (BERT) to detect fake news in news articles from various sources. BERT topic modeling is applied to cluster news articles by their respective topics, followed by summarization to measure the similarity scores. The hypothesis posits that topics with larger variances are more likely to contain fake news. The proposed method was evaluated using a dataset of approximately 1000 labeled news articles related to the Syrian war. The study found that while unsupervised content clustering with topic similarity was insufficient to detect fake news, it demonstrated the prevalence of fake news content and its potential for clustering by topic