ABSTRACT
Extracting information from textual data of news articles has been proven to be significant in developing efficient fake news detection systems. Pointedly, to fight disinformation, researchers concentrated on extracting information which focuses on exploiting linguistic characteristics that are common in fake news and can aid in detecting false content automatically. Even though these approaches were proven to have high performance, the research community proved that both the language as well as the word use in literature are evolving. Therefore, the objective of this paper is to explore the linguistic characteristics of fake news and real ones over time. To achieve this, we establish a large dataset containing linguistic characteristics of various articles over the years. In addition, we introduce a novel framework where the articles are classified in specified topics based on their content and the most informative linguistic features are extracted using dimensionality reduction methods. Eventually, the framework detects the changes of the extracted linguistic features on real and fake news articles over the time incorporating a novel change-point detection method. By employing our framework for the established dataset, we noticed that the linguistic characteristics which concern the article's title seem to be significantly important in capturing important movements in the similarity level of "Fake" and "Real" articles.
ABSTRACT
Sentiment analysis is an evolving field of study that employs artificial intelligence techniques to identify the emotions and opinions expressed in a given text. Applying sentiment analysis to study the billions of messages that circulate in popular online social media platforms has raised numerous opportunities for exploring the emotional expressions of their users. In this paper we combine sentiment analysis with natural language processing and topic analysis techniques and conduct two different studies to examine whether engagement in entrepreneurship is associated with more positive emotions expressed on Twitter. In study 1, we investigate three samples with 6.717.308, 13.253.244, and 62.067.509 tweets respectively. We find that entrepreneurs express more positive emotions than non-entrepreneurs for most topics. We also find that social entrepreneurs express more positive emotions, and that serial entrepreneurs express less positive emotions than other entrepreneurs. In study 2, we use 21.491.962 tweets to explore 37.225 job-status changes by individuals who entered or quit entrepreneurship. We find that a job change to entrepreneurship is associated with a shift in the expression of emotions to more positive ones.
Subject(s)
Emotions , Entrepreneurship , Social Media , Humans , London , Los Angeles , Regression AnalysisABSTRACT
A sampling, modulation, and separation (SMS) unit was tested for detection of hazardous chemicals. The SMS unit, designed and developed for on-site sampling and analysis, consists of a dynamic inlet system coupled with a fast, miniaturized gas chromatograph (GC). Feasibility of the SMS unit was evaluated together with a hazardous chemical vapor generator. The performance of the SMS unit was tested with automated thermal desorption after SMS to collect samples for GC-mass spectrometry (GC-MS) measurements. Detection of sarin nerve agent was verified. Additionally, the vapor generator was connected to the SMS unit, which was hyphenated with a photoionization detector (PID), thus creating a fast GC-PID system. This system gave a positive response for degradation products of sulfur mustard, thereby indicating suitability of the SMS-PID unit for field drone applications.
