Persistence of Risk Awareness: Manchester Area Bombing on 22 May 2017

Every time a significant societal catastrophe occurs, the resulting trauma intensifies the sense of risk awareness, which often wanes in the public consciousness over time. Even the most widely covered and important events, though, can fade from people's memories over time or even become the topic of false information. A subjective reality regarding the event, its causes, and its effects may be created as a result of cognitive biases and the dependence on shortcuts that these characteristics of human cognition induce. These biases can cause erroneous judgments and other types of irrationality if they are not addressed. Information on these events can be spread through digital technologies, which are currently opening up new avenues for information exchange. The historical event which is a case study of our research took place on May 22, 2017, at the Manchester Arena concert venue, more than five years ago. This raises concerns about the way in which these cognitive biases are being addressed through information webs. What are the trends in how people use websites like Wikipedia to find information about catastrophic events like the Manchester bombing? Is there a connection between the purpose of individuals to use social media to look up more details about an event after it has been covered in the media? What are the temporal dynamics of the traffic on the Wikipedia page for the Manchester bombing? Our analysis of the Wikipedia traffic data shows persistent interest in this historical event with seasonal picks on Memorial Day

COVID-19 Conspiracy Theories Discussion on Twitter

The coronavirus disease 2019 (COVID-19) pandemic was an unexpected event and resulted in catastrophic consequences with long-lasting behavioral effects. People began to seek explanations for different aspects of COVID-19 and resorted to conspiracy narratives. The objective of this article is to analyze the changes on the discussion of different COVID-19 conspiracy theories throughout the pandemic on Twitter. We have collected a data set of 1.269 million tweets associated with the discussion on conspiracy theories between January 2020 and November 2021. The data set includes tweets related to eight conspiracy theories: the 5G, Big Pharma, Bill Gates, biological weapon, exaggeration, FilmYourHospital, genetically modified organism (GMO), and the vaccines conspiracy. The analysis highlights several behaviors in the discussion of conspiracy theories and allows categorizing them into four groups. The first group are conspiracy theories that peaked at the beginning of the pandemic and sharply declined afterwards, including the 5G and FilmYourHospital conspiracies. The second group associated with the Big Pharma and vaccination-related conspiracy whose role increased as the pandemic progressed. The third are conspiracies that remained persistent throughout the pandemic such as exaggeration and Bill Gates conspiracies. The fourth are those that had multiple peaks at different times of the pandemic including the GMO and biological weapon conspiracies. In addition, the number of COVID-19 new cases was found to be a significant predictor for the next week tweet frequency for most of the conspiracies

Mining and investigating the factors influencing crowdfunding success

Crowdfunding is an innovative and relatively new financial method that connects entrepreneurs and investors through the Internet. It allows entrepreneurs to raise often small amounts of funds from a large number of investors to finance start-ups. The gaming industry is a suitable market for crowdfunding and has uniquely interesting characteristics that are worthy of exploration. The article examines which factors can influence the success of crowdfunding campaigns in this industry. The study uses principal components analysis, logistic regression and the OneRule method to analyze 9962 projects between 2009 and 2018. Several attributes, including textual variables are identified that influence the success of crowdfunding campaigns. The findings provide valuable insights for the success surrounding such campaigns and have implications for practice

Contribution to the Global Digital Compact: “Digital commons as a global public good. Internet as a free space, and methods for combating the spread of disinformation and misinformation.”

The Internet as a common good implies the absence of any restrictions, closures, and blockages with censorship being unacceptable in democratic societies. However, it can lead to the uncontrolled growth and spread of disinformation and misinformation, which can have negative effects on democratic processes, on emergency management, and on human rights. While part of society sees the Internet as the last free space and considers the restriction of the Internet an infringement of citizens’ rights to freedom of communication and information, another part of society advocates at least reasonable censorship of the Internet. Parallel to this is the question of who will be behind the censorship – will it be the government, private companies, platforms, or search engines, and what will be the rules and algorithms of censorship. As part of its participation in the CORE project (sCience&human factOr for Resilient sociEty), IIASA held an online consultation with project participants to discuss the topic of “Internet as a free space and methods for combating the spread of disinformation and misinformation” and to prepare key principles and commitments as a contribution to the Global Digital Compact. This report provides a comprehensive overview of the key points raised by the participants in the consultation proces

Semi-Supervised Learning Classifier for Misinformation Related to Earthquakes Prediction on Social Media

Social media is a fertile ground for the growth and distribution of misinformation. The belief in misinformation can have devastating consequences, and may lead to unnecessary loss of life. Properly identifying and countering misinformation on social media is therefore necessary for the fight against misinformation. In this research, we developed an Adjusted Semi-Supervised Learning for Social Media (ASSLSM) method to classify and analyze tweets regarding misinformation related to earthquakes prediction. The ASSLSM method adjusts the pseudo-labeling constraints based on assumptions related to metadata of the tweets and users, with the goal of providing better information to the underlying models. We collected a dataset of 82,129 tweets related to the subject of earthquakes prediction. Expert seismologists manually labeled 4,157 tweets. We evaluated and compared the performance of ASSLSM, supervised learning, and semi-supervised learning (SSL) methods on the dataset. We found that the ASSLSM methodology provides better and more consistent performance in comparison to supervised learning and SSL. Finally, we used an ASSLSM classifier to classify the full dataset and analyzed the classified dataset

Mining the Discussion of Monkeypox Misinformation on Twitter Using RoBERTa

The monkeypox outbreak in 2022 raised uncertainty leading to misinformation and conspiracy narratives in social media. The belief in misinformation leads to poor judgment, decision making, and even to unnecessary loss of life. The ability of misinformation to spread through social media may worsen the harms of different emergencies, and fighting it is therefore critical. In this work, we analyzed the discussion of misinformation related to monkeypox on Twitter by training different classifiers that differentiate between tweets that spread and tweets that counter misinformation. We collected over 1.4M tweets related to the discussion of monkeypox on Twitter from over 500K users and calculated word and sentence embeddings using Natural Language Processing (NLP) methods. We trained multiple machine learning classification models and fine-tuned a Robustly Optimized BERT Pretraining Approach (RoBERTa) model on a set of 3K hand-labeled tweets. We found that the fine-tuned RoBERTa model provided superior results and used it to classify the complete dataset into three categories, namely misinformation, counter misinformation and neutral. We analyzed the behavioral patterns and domains that were used in misinformation and counter misinformation tweets. The findings provide insights into the scale of misinformation within the discussion on monkeypox and the behavior of tweets and users that spread and counter misinformation over time. In addition, the findings allow us to derive policy recommendations to address misinformation in social media