The COVID-19 pandemic brought unforeseen challenges that could forever change the way societies prioritize and deal with public health issues. The approaches to contain the spread of the virus have entailed governments issuing recommendations on social distancing, lockdowns to restrict movements, and suspension of services. There are concerns that the COVID-19 crisis and the measures adopted by countries in response to the pandemic may have led to an upsurge in violence against children. Added stressors placed on caregivers, economic uncertainty, job loss or disruption to livelihoods and social isolation may have led to a rise in children’s experience of violence in the home. Extended online presence by children may have resulted in increased exposure to abusive content and cyberbullying. This study uses testimonial-based and conversational-based data collected from social media users. Conversations on Twitter were reviewed to measure increases in abusive or hateful content, and cyberbullying, while testimonials from Reddit forums were examined to monitor changes in references to family violence before and after the start of the stay-at-home restrictions. Violence-related subreddits were among the topics with the highest growth after the COVID-19 outbreak. The analysis of Twitter data shows a significant increase in abusive content generated during the stay-at-home restrictions. The collective experience of the COVID-19 pandemic and related containment measures offers insights into the wide-ranging risks that children are exposed to in times of crisis. As societies shift towards a new normal, which places emerging technology, remote working and online learning at its center, and in anticipation of similar future threats, governments and other stakeholders need to put in place measures to protect children from violence.

The increasingly widespread usage of the Internet and social networks has changed the way people interact with each other and react to events. These interactions enable positive collective outcomes such as enhancing collaboration in science. Conversely, undesirable effects have emerged: the self-segregation of online users within “bubbles” of biased content, the spread of misinformation, and the growing diffusion of aggressive speech with radical emotional valence. Affective polarization is the extent to which two opposing groups dislike one another, and it could be measured as the degree to which the two groups are willing to discriminate one against the other. Such social mechanism could occur in online social networks as a result of a controversial event in the offline world. To counter affective polarization, influential actors often make interventions using counter narratives in online social networks. However, a quantitative measure of the effectiveness of such counter narratives is typically not provided. In this study, we propose an approach to evaluate the affective polarization in online discussions generated by an offline controversial event and a measure of the effectiveness of counter narratives made by influential actors to attenuate the rise of affective polarization. The proposed approach was applied to five cases of controversial events that occurred in European soccer leagues using data collected from Twitter. Such an approach could be generalized to any other scenario involving an offline event that sparks divergent emotional reactions in online discussions and an official social media account that intervenes with a counter narrative to impact affective polarization.

The criticality of sustainable development to control the unprecedented consequences of climate change is clear. A vital element in launching sustainability projects is financing, especially for projects by small and medium enterprises. The first and crucial step to offering financing services for sustainable development is to identify and evaluate promising projects. The current practice to accomplish this step heavily depends on subject-matter expertise and professional networks. The current practice also involves extensive manual document reviews and subjective decisions. Therefore, existing methods are time-consuming, inefficient, and not scalable. This study proposes an automated system to identify potential sustainability projects for financing services using Artificial Intelligence (AI). The proposed method uses web crawlers and text mining solutions, including Natural Language Processing (NLP), to search the Internet, analyze text data, evaluate the information quantitatively, and identify potential sustainability projects for financing services. The proposed method was implemented and empirically assessed. The results indicate that the AI-enhanced system is able to identify and prioritize potential sustainability projects with 87% accuracy. The outcomes of this study will help financial experts and decision-makers take advantage of the information available on the Internet efficiently to improve the existing methods for identifying potential projects for financing services.

Goal: The United States (US) is currently one of the countries hardest-hit by the novel SARS-CoV-19 virus. One key difficulty in managing the outbreak at the national level is that due to the US' diversity, geographic spread, and economic inequality, the COVID-19 pandemic in the US acts more as a series of diverse regional outbreaks rather than a synchronized homogeneous one. Method: In order to determine how to assess regional risk related to COVID-19, a two-phase modeling approach is developed while considering demographic and economic criteria. First, an unsupervised clustering technique, specifically k-means, is employed to group US counties based on demographic and economic similarities. Then, time series series forecasting of each cluster of counties is developed to assess the short-run viral transmissibility risk. Results: To this end, we test ARIMA and Seasonal Trend Random Walk forecasts to determine which is more appropriate for modeling the spread and lethality of COVID-19. From our analysis, we then utilize the superior ARIMA models to forecast future COVID-19 trends in the clusters, and present the areas in the US which have the highest COVID-19 related risk heading into the winter of 2020. Conclusion: Including sub-national socioeconomic characteristics to data-driven COVID-19 infection and fatality forecasts may play a key role in assessing the risk associated with changes in infection patterns at the national level.

Despite the growing capabilities of large language models, concerns exist about the biases they develop. In this paper, we propose a novel, automated mechanism for debiasing through specified dataset augmentation in the lens of bias producers that can be useful in a variety of industries, especially ones that are “restricted” and have limited data. We consider that bias can occur due to intrinsic model architecture and dataset quality. The two aspects are evaluated using two different metrics we created. We show that our dataset augmentation algorithm reduces bias as measured by our metrics. Our code can be found on an online GitHub repository.

The emergence of social media has facilitated new ways of communication and social engagement. Political elites and public figures use social media to inform and mobilize users. Discussions appearing around these tweets reflect valuable information from the communities, their interests, and concerns. The majority of the studies on conversational dynamics on social media use the network between users to track the flow of information and impact. In this study, a different approach is introduced in which discussion threads as the network between tweets are considered as the main components of the analysis. Based on this approach, a novel framework is proposed to provide a high-level overview of a Twitter stream. The metric energy is introduced to quantify user engagement in discussions initiated by each tweet and to study the patterns of user-participation overtime. To mine the content of the tweets, some recently developed text analysis methods are integrated into our model to extract the topic of the tweets, the sentiment of the tweets, the stance of tweets toward a controversial topic, and the coherency between the tweet-reply pairs. Three different data sets from politics, healthcare, and sport are collected to be used as case studies. The proposed framework provides a better understanding of how opinions form around a tweet, how different topics to bond together and make the whole discussions, for which topics discussions are more coherent, and which users are able to initiate a cross cutting and coherent conversation over social media.