DEMO: Using TwitterTrails.com to Investigate Rumor Propagation

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 demo TWITTERTRAILS, an interactive, webbased tool (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 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 TWITTERTRAILS as valuable tool for individual use, and especially for amateur and professional journalists investigating recent and breaking stories

Hoaxy: A Platform for Tracking Online Misinformation

Massive amounts of misinformation have been observed to spread in uncontrolled fashion across social media. Examples include rumors, hoaxes, fake news, and conspiracy theories. At the same time, several journalistic organizations devote significant efforts to high-quality fact checking of online claims. The resulting information cascades contain instances of both accurate and inaccurate information, unfold over multiple time scales, and often reach audiences of considerable size. All these factors pose challenges for the study of the social dynamics of online news sharing. Here we introduce Hoaxy, a platform for the collection, detection, and analysis of online misinformation and its related fact-checking efforts. We discuss the design of the platform and present a preliminary analysis of a sample of public tweets containing both fake news and fact checking. We find that, in the aggregate, the sharing of fact-checking content typically lags that of misinformation by 10--20 hours. Moreover, fake news are dominated by very active users, while fact checking is a more grass-roots activity. With the increasing risks connected to massive online misinformation, social news observatories have the potential to help researchers, journalists, and the general public understand the dynamics of real and fake news sharing.Comment: 6 pages, 6 figures, submitted to Third Workshop on Social News On the We

The Fake News Spreading Plague: Was it Preventable?

In 2010, a paper entitled "From Obscurity to Prominence in Minutes: Political Speech and Real-time search" won the Best Paper Prize of the Web Science 2010 Conference. Among its findings were the discovery and documentation of what was termed a "Twitter-bomb", an organized effort to spread misinformation about the democratic candidate Martha Coakley through anonymous Twitter accounts. In this paper, after summarizing the details of that event, we outline the recipe of how social networks are used to spread misinformation. One of the most important steps in such a recipe is the "infiltration" of a community of users who are already engaged in conversations about a topic, to use them as organic spreaders of misinformation in their extended subnetworks. Then, we take this misinformation spreading recipe and indicate how it was successfully used to spread fake news during the 2016 U.S. Presidential Election. The main differences between the scenarios are the use of Facebook instead of Twitter, and the respective motivations (in 2010: political influence; in 2016: financial benefit through online advertising). After situating these events in the broader context of exploiting the Web, we seize this opportunity to address limitations of the reach of research findings and to start a conversation about how communities of researchers can increase their impact on real-world societal issues

Early and often: Can real-time intervention by trusted authorities help stop a tsunami of disinformation?

A tsunami of disinformation is washing over the world, with social media helping it to spread quickly and widely. The purveyors of disinformation use it to press their agenda by adding untruths where previously there were none, fabricating stories, reporting them out of context, or doctoring images to promote their message. In the past, disinformation has been a prelude to and run concurrently with other attacks, including cyber and conventional warfare, and when officials reacted to disinformation, they successfully slowed its flow but did not entirely stop it, and may not have “won” cyber or conventional battles. Researchers say even multiple corrections don’t fully stop disinformation, and sowing skepticism by forewarning of a probable disinformation campaign is the most successful way of staunching the flow. Tools have been developed to help detect disinformation rapidly but officials often don’t have a plan to track, correct or refute it

Investigating the Macedonia naming dispute in the Twitter era: implications for the Greek identity crisis

The Macedonia naming dispute has been an important issue in Greek affairs. It constitutes both an irresolvable, decades-old international problem and a significant, yet undertheorised, analytical topic. In this context, our aim is to critically explore, highlight and discuss the deep-seated and pervasive patterns, representations, attitudes, beliefs, ideas and norms within the Greek social imaginary, as these emerged on Twitter in real-time, during the mass “Macedonia rally” on February 4, 2018. More specifically, drawing on the dialectical interaction between Twitter posts, sociopolitical behaviours and interpretative analytic frames linked to interdisciplinary theoretical discourses, we attempt to understand and interrogate the intellectual structures, value system and operational categories of a large number of Greek groups on the ‘Twittersphere’. Based on the assumption that, in the last instance, the rigid refusal of the majority of the Greek people to accept a ‘composite name’ solution is connected with the tacit social imaginary of the Greek society, the present paper brings to the fore a complex identity problem. This problem relationally refers to the internal workings of the individuals, the psyche and the unconscious, but also to hidden and unreflected symbolic backgrounds, macro-social processes, and cultural legacies. Our following Twitter network analysis, focused on selected hashtags regarding the ‘Macedonia rally’, point out the character of social dynamics and ascertain the findings of the interpretative research strand

The function of competitive intelligence in South African insurance post-COVID-19 outbreak

Background: Competitive intelligence (CI) involves monitoring competitors and providing organisations with actionable and meaningful intelligence. Some studies have focussed on the role of CI in other industries post-COVID-19 pandemic. Objectives: This article aims to examine the impact of COVID-19 on the South African insurance sector and how the integration of CI and related technologies can sustain the South African insurance sector post-COVID-19 epidemic. Method: Qualitative research with an exploratory-driven approach was used to examine the impact of the COVID-19 pandemic on the South African insurance sector. Qualitative secondary data analyses were conducted to measure insurance claims and death benefits paid during the COVID-19 pandemic. Results: The research findings showed that the COVID-19 pandemic significantly impacted the South African insurance industry, leading to a reassessment of pricing, products, and risk management. COVID-19 caused disparities in death benefits and claims between provinces; not everyone was insured. Despite challenges, South African insurers remained well-capitalised and attentive to policyholders. Integrating CI and analytical technologies could enhance the flexibility of prevention, risk management, and product design. Conclusion: COVID-19 requires digital transformation and CI for South African insurers’ competitiveness. Integrating artificial intelligence (AI), big data (BD), and CI enhances value, efficiency, and risk assessments. Contribution: This study highlights the importance of integrating CI strategies and related technologies into South African insurance firms’ operations to aid in their recovery from the COVID-19 crisis. It addresses a research gap and adds to academic knowledge in this area

Social media analytics with applications in disaster management and COVID-19 events

Social media such as Twitter offers a tremendous amount of data throughout an event or a disastrous situation. Leveraging social media data during a disaster is beneficial for effective and efficient disaster management. Information extraction, trend identification, and determining public reactions might help in the future disaster or even avert such an event. However, during a disaster situation, a robust system is required that can be deployed faster and process relevant information with satisfactory performance in real-time. This work outlines the research contributions toward developing such an effective system for disaster management, where it is paramount to develop automated machine-enabled methods that can provide appropriate tags or labels for further analysis for timely situation-awareness. In that direction, this work proposes machine learning models to identify the people who are seeking assistance using social media during a disaster and further demonstrates a prototype application that can collect and process Twitter data in real-time, identify the stranded people, and create rescue scheduling. In addition, to understand the people’s reactions to different trending topics, this work proposes a unique auxiliary feature-based deep learning model with adversarial sample generation for emotion detection using tweets related to COVID-19. This work also presents a custom Q&A-based RoBERTa model for extracting related phrases for emotions. Finally, with the aim of polarization detection, this research work proposes a deep learning pipeline for political ideology detection leveraging the tweet texts and the expressed emotions in the text. This work also studies and conducts the historical emotion and polarization analysis of the COVID-19 pandemic in the USA and several individual states using tweeter data --Abstract, page iv