Engage students in news writing

The technologies evolution impacts how information is produced and consumed by users. Nonetheless, with the spread of information content available on most online news platforms, the misinformation increases alongside the less credible content. In this scope, the present research aims to develop a technological ecosystem to promote students’ writing ability. The system will help students, search for credible content to create school newspapers. Thus, in this article, the architecture of the solution for news writing tool for the Portuguese language is presented. This paper aims to introduce a constructive approach that presents the system architecture that will support the development of a news creation tool.publishe

Interrupting The Propaganda Supply Chain

In this early-stage research, a multidisciplinary approach is presented for the detection of propaganda in the media, and for modeling the spread of propaganda and disinformation using semantic web and graph theory. An ontology will be designed which has the theoretical underpinnings from multiple disciplines including the social sciences and epidemiology. An additional objective of this work is to automate triple extraction from unstructured text which surpasses the state-of-the-art performance

Linguistic Features and Bi-LSTM for Identification of Fake News

With the spread of Internet technologies, the use of social media has increased exponentially. Although social media has many benefits, it has become the primary source of disinformation or fake news. The spread of fake news is creating many societal and economic issues. It has become very critical to develop an effective method to detect fake news so that it can be stopped, removed or flagged before spreading. To address the challenge of accurately detecting fake news, this paper proposes a solution called Statistical Word Embedding over Linguistic Features via Deep Learning (SWELDL Fake), which utilizes deep learning techniques to improve accuracy. The proposed model implements a statistical method called “principal component analysis” (PCA) on fake news textual representations to identify significant features that can help identify fake news. In addition, word embedding is employed to comprehend linguistic features and Bidirectional Long Short-Term Memory (Bi-LSTM) is utilized to classify news as true or fake. We used a benchmark dataset called SWELDL Fake to validate our proposed model, which has about 72,000 news articles collected from different benchmark datasets. Our model achieved a classification accuracy of 98.52% on fake news, surpassing the performance of state-of-the-art deep learning and machine learning models

Comparative study on sentimental analysis using machine learning techniques

With the advancement of the Internet and the world wide web (WWW), it is observed that there is an exponential growth of data and information across the internet. In addition, there is a huge growth in digital or textual data generation. This is because users post the reply comments in social media websites based on the experiences about an event or product. Furthermore, people are interested to know whether the majority of potential buyers will have a positive or negative experience on the event or the product. This kind of classification in general can be attained through Sentiment Analysis which inputs unstructured text comments about the product reviews, events, etc., from all the reviews or comments posted by users. This further classifies the data into different categories namely positive, negative or neutral opinions. Sentiment analysis can be performed by different machine learning models like CNN, Naive Bayes, Decision Tree, XgBoost, Logistic Regression etc. The proposed work is compared with the existing solutions in terms of different performance metrics and XgBoost outperforms out of all other methods

COCAINE SEIZURES AND CRIME: DATA ANALYTICS USING BIG DATA TOOLS

Includes Supplementary MaterialColombia's status as the largest cocaine producer in the world has prompted its government's strategies to combat drug trafficking. One of these strategies is to seize cocaine in the Colombian jurisdictional territory. The unintended consequences of this strategy on crime rates, particularly homicides, remain uncertain. Web scraping methods and big data tools were used to gather and construct a time series dataset on cocaine seizures from three distinct websites, while the homicides dataset was supplied by the Colombian Ministry of Defense (MDN). This study aims to investigate, from a quantitative standpoint, whether there is a link between cocaine seizures and homicides in the Colombian Pacific region, utilizing an exploratory data analysis (EDA) method and machine learning techniques. The study recognizes the constraints of the sample size and opts to reveal valuable insights through data analysis and modeling instead. Despite the constraints, two models were developed to partially explicate the significance of this correlation. The study's findings provide value for policymakers, military personnel, government officials, and academics, offering essential perspectives to devise improved policies and strategies to mitigate drug trafficking in the Colombian Pacific region without exacerbating homicide rates. Future research endeavors could consider expanding the sample size of the cocaine seizure time-series dataset to conduct a more robust correlation analysis.Approved for public release. Distribution is unlimited.Outstanding ThesisCapitan de Corbeta, Colombian National Nav