Investigating Rumor Propagation with TwitterTrails

Social media have become part of modern news reporting, used by journalists to spread information and find sources, or as a news source by individuals. The quest for prominence and recognition on social media sites like Twitter can sometimes eclipse accuracy and lead to the spread of false information. As a way to study and react to this trend, we introduce {\sc TwitterTrails}, an interactive, web-based tool ({\tt twittertrails.com}) that allows users to investigate the origin and propagation characteristics of a rumor and its refutation, if any, on Twitter. Visualizations of burst activity, propagation timeline, retweet and co-retweeted networks help its users trace the spread of a story. Within minutes {\sc TwitterTrails} will collect relevant tweets and automatically answer several important questions regarding a rumor: its originator, burst characteristics, propagators and main actors according to the audience. In addition, it will compute and report the rumor's level of visibility and, as an example of the power of crowdsourcing, the audience's skepticism towards it which correlates with the rumor's credibility. We envision {\sc TwitterTrails} as valuable tool for individual use, but we especially for amateur and professional journalists investigating recent and breaking stories. Further, its expanding collection of investigated rumors can be used to answer questions regarding the amount and success of misinformation on Twitter.Comment: 10 pages, 8 figures, under revie

Unleashing the Power of User Reviews: Exploring Airline Choices at Catania Airport, Italy

This study aims to investigate the possible relationship between the mechanisms of social influence and the choice of airline, through the use of new tools, with the aim of understanding whether they can contribute to a better understanding of the factors influencing the decisions of consumers in the aviation sector. We have chosen to extract user reviews from well-known platforms: Trustpilot, Google, and Twitter. By combining web scraping techniques, we have been able to collect a comprehensive dataset comprising a wide range of user opinions, feedback, and ratings. We then refined the BERT model to focus on insightful sentiment in the context of airline reviews. Through our analysis, we observed an intriguing trend of average negative sentiment scores across various airlines, giving us deeper insight into the dynamics between airlines and helping us identify key partnerships, popular routes, and airlines that play a central role in the aeronautical ecosystem of Catania airport during the specified period. Our investigation led us to find that, despite an airline having received prestigious awards as a low-cost leader in Europe for two consecutive years 2021 and 2022, the "Catanese" user tends to suffer the dominant position of other companies. Understanding the impact of positive reviews and leveraging sentiment analysis can help airlines improve their reputation, attract more customers, and ultimately gain a competitive edge in the marketplace.Comment: arXiv admin note: text overlap with arXiv:1311.3475 by other author

Effectiveness of Social Media Analytics on Detecting Service Quality Metrics in the U.S. Airline Industry

During the past few decades, social media has provided a number of online tools that allow people to discuss anything freely, with an increase in mobile connectivity. More and more consumers are sharing their opinions online with others. Electronic Word of Mouth (eWOM) is the virtual communication in use; it plays an important role in customers’ buying decisions. Customers can choose to complain or to compliment services or products on their social media platforms, rather than to complete the survey offered by the providers of those services. Compared with the traditional survey, or with the air travel customer report published by U.S. Department of Transportation (DOT) each month, social media offers features that can spread information quickly and broadly. This dissertation offers a novel methodology that, by utilizing emotional sentiment analysis, can help the airline industry to improve its service quality. Longitudinal data, retrieved from Twitter, are collected from twelve U.S.-based airline companies, in order to represent airline companies in different levels and categories. The data covers three consecutive months in Quarter 2 of 2017. Applied alongside the service quality metrics of the airline industry, the benchmark datasets for each metric are created. The purpose of this dissertation is to bridge the gap in traditional methodology for a service quality measurement in the airline industry and to demonstrate the way in which socialized textual data can measure the quality of the service offered by airline service providers. In addition, sentiment analysis is applied, in order to get the sentiment score of each tweet. Emotional lexicons are used to detect the emotion expressed by the tweet in two emotional dimensions: each tweet’s Valence and Arousal are calculated. Once the SERVQUAL model is applied and the keywords to find the corresponding social media data are created for each dimension, the results show that responsiveness, assurance, and reliability are positively correlated to the AQR score that measures the service quality of airline industry. This study also finds that a large amount of negative social media data will negatively affect the AQR score. Finally, this study finds that the interaction of the sentiment score and the arousal score of textual social media data play the important role in predicting the service quality of the airline industry. Finally, an opinion-oriented information system is proposed. In the last, this study provides theory verification of SERVQUAL

Text Analysis of Air Force References in Twitter

Social media has grown to become a rich source for opinions, authored by individuals who volunteer them, unedited and in real-time. Armed with this information, an organization like the Air Force can understand the perceptions of consumers and learn to better serve the American taxpayer. To accomplish this goal, this research takes a qualitative approach, utilizing social media analytics in combination with various Text Mining methodologies (word frequency, word relationships, sentiment analysis, topic modeling) to provide insight on Air Force related content shared on Twitter. To provide a well-rounded analysis of the overall perception of the Air Force enterprise, the methods mentioned are conducted on Tweets related to the Air Force’s five core missions: Space/Cyberspace, Nuclear Deterrence, Air Superiority, Advancements in Technology, and Intelligence, Surveillance, Reconnaissance. This research also identifies the key players that publish the most engaged Tweets related to the Air Force. By understanding the types of users who possess the most influence (Regular Users, Bloggers, Celebrities, Military Leaders, Politicians, Professional Organizations), Air Force leaders are better equipped to react to content and protect the Air Force brand

Exploring the impact of COVID-19 on aviation industry: A text mining approach

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