Optimization Algorithms for Chemoinformatics and Material-informatics

Modeling complex phenomena in chemoinformatics and material-informatics can often be formulated as single-objective or multi-objective optimization problems (SOOPs or MOOPs). For example, the design of new drugs or new materials is inherently a MOOP since drugs/materials require the simultaneous optimization of multiple parameters

Cyber-echoes of climate crisis: Unraveling anthropogenic climate change narratives on social media

Social media platforms have a key role in spreading narratives about climate change, and therefore it is crucial to understand the discussion about climate change in social media. The discussion on anthropogenic climate change in general, and social media specifically, has multiple different narratives. Understanding the discourses can assist efforts of mitigation, adaptation, and policy measures development. In this work, we collected 333,635 tweets in English about anthropogenic climate change. We used Natural Language Processing (NLP) and machine learning methods to embed the semantic meaning of the tweets into vectors, cluster the tweets, and analyze the results. We clustered the tweets into four clusters that correspond to four narratives in the discussion. Analyzing the behavioral dynamics of each cluster revealed that the clusters focus on the discussion of whether climate change is caused by humans or not, scientific arguments, policy, and conspiracy. The research results can serve as input for media policy and awareness-raising measures on climate change mitigation and adaptation policies, and facilitating future communications related to climate change

The Discussions of Monkeypox Misinformation on Social Media

The global outbreak of the monkeypox virus was declared a health emergency by the World Health Organization (WHO). During such emergencies, misinformation about health suggestions can spread rapidly, leading to serious consequences. This study investigates the relationships between tweet readability, user engagement, and susceptibility to misinformation. Our conceptual model posits that tweet readability influences user engagement, which in turn affects the spread of misinformation. Specifically, we hypothesize that tweets with higher readability and grammatical correctness garner more user engagement and that misinformation tweets tend to be less readable than accurate information tweets. To test these hypotheses, we collected over 1.4 million tweets related to monkeypox discussions on X (formerly Twitter) and trained a semi-supervised learning classifier to categorize them as misinformation or not-misinformation. We analyzed the readability and grammar levels of these tweets using established metrics. Our findings indicate that readability and grammatical correctness significantly boost user engagement with accurate information, thereby enhancing its dissemination. Conversely, misinformation tweets are generally less readable, which reduces their spread. This study contributes to the advancement of knowledge by elucidating the role of readability in combating misinformation. Practically, it suggests that improving the readability and grammatical correctness of accurate information can enhance user engagement and consequently mitigate the spread of misinformation during health emergencies. These insights offer valuable strategies for public health communication and social media platforms to more effectively address misinformation

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

A dual-focus analysis of wikipedia traffic and linguistic patterns in public risk awareness Post-Charlie Hebdo

This study investigates the dynamics of public risk awareness in the aftermath of the Charlie Hebdo terrorist attack on January 7, 2015, through a dual-focus analysis of Wikipedia traffic and Google Trends data. Analyzing the temporal patterns of Wikipedia page views in both English and French, sheds light on how significant media events, anniversaries, and related incidents influence public engagement with terrorism-related content over time. The study highlights the critical role of linguistic and cultural factors in shaping these patterns, revealing that Francophone regions, particularly France and its former colonies, exhibit a more sustained and consistent interest in the Charlie Hebdo event compared to Anglophone regions. The heightened engagement in French-speaking areas suggests that cultural and historical ties influence public risk perception and awareness. Complementing this analysis with geographic insights from Google Trends, the study provides a more comprehensive understanding of how people in different regions perceive and respond to terrorism. The findings underscore the importance of digital platforms in gauging public awareness and suggest practical implications for designing targeted risk communication strategies. These strategies could be timed to coincide with moments of heightened public interest, such as anniversaries, to enhance public resilience and preparedness in the face of terrorism. This study contributes to the broader understanding of digital media's role in shaping and sustaining public risk awareness in a global context

Persistence of Rumours and Hate Speech Over the Years: the Manchester Arena Bombing

Following the 2017 Manchester Arena bombing, the ensuing discussions in the media and on social platforms highlighted the potential of terrorism to deepen societal divisions. This study investigates the dynamics of rumors on social media and in the press after the attack, as well as the subsequent discourse on migration policies. We compiled a dataset comprising 3,184 press articles and 89,148 tweets pertaining to the Manchester Arena bombing. The research aims to identify prevalent rumors, assess their short- and long-term effects on user engagement, analyze the sentiment in tweets related to each rumor, and scrutinize perceptions of terrorism threats and migration policies among both the press and Twitter users. The findings reveal that Twitter acted as an echo chamber for misinformation, amplifying specific rumors related to the attack, while the press demonstrated fact-checking practices and offered nuanced perspectives. Notably, one rumor suggesting the attacker was a refugee gained traction over time, reflecting a surge in anti-immigrant sentiments. Emotional responses on Twitter varied from a neutral tone to heightened distress and anger, underscoring the significant impact of social media narratives on public sentiment. The research highlights the polarization of views on social media, influenced by the concise format of tweets and the rapid production cycle, with Twitter users predominantly expressing very negative attitudes toward immigration. This study emphasizes the crucial role of the media in dispelling misinformation and cultivating a nuanced public understanding in complex socio-political contexts