Enriching Social Sharing for the Dementia Community: Insights from In-Person and Online Social Programs

The dementia community faces major challenges in social engagements, which have been further complicated by the prolonged physical distancing measures due to the COVID-19 pandemic. Designing digital tools for in-person social sharing in family and care facility settings has been well explored, but comparatively little HCI work has focused on the design of community-based social technologies for virtual settings. We present our virtual fieldwork on remote social activities explored by one dementia community in response to the impacts of the pandemic. Building upon our previously published on-site fieldwork in this community, we expand on our initial publication by follow-up interviewing caregivers and facilitators and reflecting on a virtual social program. Through thematic analysis and contrasting in-person and online formats of the program, we deepened the understanding of virtual social engagements of the dementia community, examining their efforts to leverage physical objects and environments, enhance open and flexible experiences, and expand collaborative space. We propose to open new design opportunities through holistic approaches, including reimagining community social spaces, rethinking agency in people with dementia and caregivers, and diversifying HCI support across communities and stakeholders. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Measuring Community Resilience During the COVID-19 Based on Community Wellbeing and Resource Distribution

The COVID-19 pandemic has severely harmed every aspect of our daily lives, resulting in a slew of social problems. Therefore, it is critical to accurately assess the current state of community functionality and resilience under this pandemic for successful recovery. To this end, various types of social sensing tools, such as tweeting and publicly released news, have been employed to understand individuals' and communities' thoughts, behaviors, and attitudes during the COVID-19 pandemic. However, some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like COVID-19. This paper aims to assess the correlation between various news and tweets collected during the COVID-19 pandemic on community functionality and resilience. We use fact-checking organizations to classify news as real, mixed, or fake, and machine learning algorithms to classify tweets as real or fake to measure and compare community resilience (CR). Based on the news articles and tweets collected, we quantify CR based on two key factors, community wellbeing and resource distribution, where resource distribution is assessed by the level of economic resilience and community capital. Based on the estimates of these two factors, we quantify CR from both news articles and tweets and analyze the extent to which CR measured from the news articles can reflect the actual state of CR measured from tweets. To improve the operationalization and sociological significance of this work, we use dimension reduction techniques to integrate the dimensions. © 2020 Tsinghua University Press.

Understanding the spatiotemporal heterogeneities in the associations between COVID-19 infections and both human mobility and close contacts in the United States

It has been well-established that human mobility has an inseparable relationship with COVID-19 infections. As social-distancing and stay-at-home orders lifted and data availability increased, our knowledge on how human behaviors including mobility and close interpersonal contacts associate with the pandemic progression also needs to stay updated. In this paper, we examine the relationship of COVID-19 daily transmissibility measured by the total confirmed cases and the effective reproduction number (Rt) with the two indices that provide human behavior insights: Cuebiq Mobility Index (CMI) and Cuebiq Contact Index (CCI). The correlations between each index and COVID-19 infections are evaluated using the Maximal Information Coefficient (MIC) which is powerful in capturing complex relationships. Moreover, the study period is segmented into three periods by Bayesian Change Point Detection to examine temporal heterogeneity and the mainland US states are grouped into three distinct clusters using the KShape clustering algorithm to further examine spatial heterogeneity. The CCI and CMI exhibited very different patterns and we found significant temporal and spatial heterogeneities among the relationships between the two indices and COVID-19 infection rate. Although human mobility has demonstrated high correlation with COVID-19 infection rate in 2020, close contacts became much more correlated with COVID-19 infection than mobility in 2021. However, states in the Plains and Rocky Mountains area are exceptions to this observation. During the first wave in 2020, it is also shown that mobility has a high impact on states outside of Farwest and Southeast than those states within that region. © 2022 ACM.

"Who can help me?": Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit

During the ongoing COVID-19 crisis, subreddits on Reddit, such as r/Coronavirus saw a rapid growth in user's requests for help (support seekers - SSs) including individuals with varying professions and experiences with diverse perspectives on care (support providers - SPs). Currently, knowledgeable human moderators match an SS with a user with relevant experience, i.e, an SP on these subreddits. This unscalable process defers timely care. We present a medical knowledge-infused approach to efficient matching of SS and SPs validated by experts for the users affected by anxiety and depression, in the context of with COVID-19. After matching, each SP to an SS labeled as either supportive, informative, or similar (sharing experiences) using the principles of natural language inference. Evaluation by 21 domain experts indicates the efficacy of incorporated knowledge and shows the efficacy the matching system.

ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks

In this paper, we present ArCOV-19, an Arabic COVID-19 Twitter dataset that spans one year, covering the period from 27th of January 2020 till 31st of January 2021. ArCOV-19 is the first publicly-available Arabic Twitter dataset covering COVID-19 pandemic that includes about 2.7M tweets alongside the propagation networks of the most-popular subset of them (i.e., most-retweeted and-liked). The propagation networks include both retweets and conversational threads (i.e., threads of replies). ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing. Preliminary analysis shows that ArCOV-19 captures rising discussions associated with the first reported cases of the disease as they appeared in the Arab world. In addition to the source tweets and propagation networks, we also release the search queries and languageindependent crawler used to collect the tweets to encourage the curation of similar datasets. © WANLP 2021 - 6th Arabic Natural Language Processing Workshop

Toxicity and the effect on digital communities

Social networks or social media platforms have been around since the 1980s. With the increase of users every year, we have seen the rise of harmful content such as rude, disrespectful, unreasonable, abusive comments, and hate speech. The increase of harmful content was even more noticeable in 2020 with COVID-19 and election topics. Mainstream social media platforms, such as Facebook, Twitter, Instagram, and YouTube, continue to increase their efforts to reduce harmful content such as hate speech, harmful content, and misinformation. However, social media platforms cannot catch and remove all toxic content from their platforms before it impacts an individual or community. This dissertation compares toxicity at the platform level for mainstream and non-mainstream social media, analyzes these differences between the two platforms, and examines toxicity at the macro level by analyzing the effect of toxicity on community dynamics for and pro and anti-COVID datasets from Twitter. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Is gender-based violence a confluence of culture? Empirical evidence from social media.

Gender-based violence (GBV) has been plaguing our society for long back. The severity of GBV has spurred research around understanding the causes and factors leading to GBV. Understanding factors and causes leading to GBV is helpful in planning and executing efficient policies to curb GBV. Past researches have claimed a country's culture to be one of the driving reasons behind GBV. The culture of a country consists of cultural norms, societal rules, gender-based stereotypes, and social taboos which provoke GBV. These claims are supported by theoretical or small-scale survey-based research that suffers from under-representation and biases. With the advent of social media and, more importantly, location-tagged social media, huge ethnographic data are available, creating a platform for many sociological research. In this article, we also utilize huge social media data to verify the claim of confluence between GBV and the culture of a country. We first curate GBV content from different countries by collecting a large amount of data from Twitter. In order to explore the relationship between a country's culture and GBV content, we performed correlation analyses between a country's culture and its GBV content. The correlation results are further re-validated using graph-based methods. Through the findings of this research, we observed that countries with similar cultures also show similarity in GBV content, thus reconfirming the relationship between GBV and culture.

Development of Bilingual Sentiment and Emotion Text Classification Models from COVID-19 Vaccination Tweets in the Philippines

Social media can be used to understand how the public is responding to the ongoing nationwide COVID-19 vaccination campaign, allowing policymakers to respond effectively through informed decisions. However, conducting social media analysis in the Philippine-context presents a challenge because natural informal conversations make use of a combination of English and local language. This study addresses this challenge by including part-of-speech tags, frequency of code switching and language dominance features to represent bilingualism in training machine learning models with COVID-19 vaccination-related Tweets for sentiment and emotion analysis. Results showed that the English-Tagalog Logistic Regression sentiment classification model performed better than Textblob, VADER and Polyglot with an accuracy of 70.36%. Similarly, the English-Tagalog SVM emotion classification model performed better than Text2emotion, NRC Affect Intensity Lexicon and EmoTFIDF with an average mean-squared error of 0.049. The added bilingual features only improved these performance metrics by a small margin. Nevertheless, SHAP analysis still revealed that sentiment and emotion classes exhibit varying levels of these bilingual features, which shows the potential in exploring similar linguistic features to distinguish between classes better during text classification for future studies. Finally, Tweets from September 2021 to January 2022 shows negative, mainly anger and sadness, perceptions towards COVID-19 vaccinations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Mining the Impacts of COVID-19 Pandemic on the Labour Market

Under the influence of the pandemic environment, many people may have lost their jobs or on the verge of being laid off, while there are many new job seekers. Hence, the status of new jobs under the pandemic and how various industries are affected by the pandemic-including predicting future work trends-have become the focus of attention. In this paper, we present a social informatics solution to mine the impacts of COVID-19 pandemic on the labour market. We make good use of data mining (especially, frequent pattern mining), statistical analysis, and prediction. Evaluation of real-life Canadian labour market data demonstrates the practicality of our tool. Although we illustrate our ideas with the Canadian labour market, our solution can be adaptable to mine labour markets in other geographical locations. © 2022 IEEE.

Data-driven Humanitarian Mapping: Harnessing Human-Machine Intelligence for High-Stake Public Policy and Resilience Planning

Humanitarian challenges, including natural disasters, food insecurity, climate change, racial and gender violence, environmental crises, the COVID-19 coronavirus pandemic, human rights violations, and forced displacements, disproportionately impact vulnerable communities worldwide. According to UN OCHA, 235 million people will require humanitarian assistance in 2021(1). Despite these growing perils, there remains a notable paucity of data science research to scientifically inform equitable public policy decisions for improving the livelihood of at-risk populations. Scattered data science efforts exist to address these challenges, but they remain isolated from practice and prone to algorithmic harms concerning lack of privacy, fairness, interpretability, accountability, transparency, and ethics. Biases in data-driven methods carry the risk of amplifying inequalities in high-stakes policy decisions that impact the livelihood of millions of people. Consequently, proclaimed benefits of data-driven innovations remain inaccessible to policymakers, practitioners, and marginalized communities at the core of humanitarian actions and global development. To help fill this gap, we propose the Data-driven Humanitarian Mapping Research Program, which focuses on developing novel data science methodologies that harness human-machine intelligence for high-stakes public policy and resilience planning.

A Parametric Multi-Agent Simulation Framework to Emulate Social Isolation During the Pandemic.

Many people worldwide have been at home for months and practicing social distancing to mitigate the spread of coronavirus (COVID-19). What may have started as a single case is now in at least 180 countries. Preliminary surveys indicate that the COVID-19 pandemic has caused people to feel more lonely and isolated than they did before. It may be due to the fear of the virus, death of loved ones, and the lock-downs restrictions imposed in some countries. This paper proposes a parametric multi-agent simulation framework to emulate Social Isolation during the pandemic. Using the proposed simulator we mimic real-world area of 144 km2 and population size of 200,000 in order to have near-accurate settings. Various parameters, such as the number of hospitals and capacity, infection rate, recovery, hospitalization, and death, are considered. The simulation is validated on a real-world scale artificial society and is parameterized to a great extent to simulate various settings.

Social Media as Information Support in Reducing COVID - 19 Depressions: Self-Efficacy as Mediator for Behavioral Modeling

With the emergence of the COVID-19 pandemic, tackling mental health issues has become challenging too. A tendency has been observed in people spending more time on social media (SM) than usual and it has become the alternative source of interaction and news sharing. Previous research shows that intensive use of SM increases stress directly or indirectly. The aim of this study is to analyze the role of self-efficacy on information support from SM and COVID-19 depressions. To achieve this objective, a quantitative analysis was performed through an online questionnaire-based survey among SM users. The findings of this study prevail that with the help of effective information support from SM and through certain behavioral modifications with users' high self-efficacy, COVID-19 stress might be lessened accordingly. © 2021 IEEE.

A Sentiment Analysis Model for COVID-19 News in Chinese Social Networks

The current prevalence of COVID-19 (coronavirus disease 2019) has produced a wide variety of responses in different regions around the world, particularly on social media and microblogs. The increasing volume of user-generated content on the social networking websites has made sentiment analysis a powerful tool for understanding the human emotional state. In this study, we perform an extensive analysis of the sentiments obtained from Chinese microblogs since the earliest reports of COVID-19. We introduce a new model for sentiment classification using Bidirectional Encoder Representations from Transformers (BERT) and obtain accuracy results over 74%. The experimental results show how the public epidemic prevention policy affects the emotions and sentiments of the public as obtained from semantic analysis of microblogs and social media during the COVID-19 pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A deep dive into COVID-19-related messages on WhatsApp in Pakistan.

The spread of COVID-19 and the lockdowns that followed led to an increase in activity on online social networks. This has resulted in users sharing unfiltered and unreliable information on social networks like WhatsApp, Twitter, Facebook, etc. In this work, we give an extended overview of how Pakistan's population used public WhatsApp groups for sharing information related to the pandemic. Our work is based on a major effort to annotate thousands of text and image-based messages. We explore how information propagates across WhatsApp and the user behavior around it. Specifically, we look at political polarization and its impact on how users from different political parties shared COVID-19-related content. We also try to understand information dissemination across different social networks-Twitter and WhatsApp-in Pakistan and find that there is no significant bot involvement in spreading misinformation about the pandemic.

Quantifying COVID-19 Content in the Online Health Opinion War Using Machine Learning.

A huge amount of potentially dangerous COVID-19 misinformation is appearing online. Here we use machine learning to quantify COVID-19 content among online opponents of establishment health guidance, in particular vaccinations ("anti-vax"). We find that the anti-vax community is developing a less focused debate around COVID-19 than its counterpart, the pro-vaccination ("pro-vax") community. However, the anti-vax community exhibits a broader range of "flavors" of COVID-19 topics, and hence can appeal to a broader cross-section of individuals seeking COVID-19 guidance online, e.g. individuals wary of a mandatory fast-tracked COVID-19 vaccine or those seeking alternative remedies. Hence the anti-vax community looks better positioned to attract fresh support going forward than the pro-vax community. This is concerning since a widespread lack of adoption of a COVID-19 vaccine will mean the world falls short of providing herd immunity, leaving countries open to future COVID-19 resurgences. We provide a mechanistic model that interprets these results and could help in assessing the likely efficacy of intervention strategies. Our approach is scalable and hence tackles the urgent problem facing social media platforms of having to analyze huge volumes of online health misinformation and disinformation.

Design and analysis of a large-scale COVID-19 tweets dataset.

As of July 17, 2020, more than thirteen million people have been diagnosed with the Novel Coronavirus (COVID-19), and half a million people have already lost their lives due to this infectious disease. The World Health Organization declared the COVID-19 outbreak as a pandemic on March 11, 2020. Since then, social media platforms have experienced an exponential rise in the content related to the pandemic. In the past, Twitter data have been observed to be indispensable in the extraction of situational awareness information relating to any crisis. This paper presents COV19Tweets Dataset (Lamsal 2020a), a large-scale Twitter dataset with more than 310 million COVID-19 specific English language tweets and their sentiment scores. The dataset's geo version, the GeoCOV19Tweets Dataset (Lamsal 2020b), is also presented. The paper discusses the datasets' design in detail, and the tweets in both the datasets are analyzed. The datasets are released publicly, anticipating that they would contribute to a better understanding of spatial and temporal dimensions of the public discourse related to the ongoing pandemic. As per the stats, the datasets (Lamsal 2020a, 2020b) have been accessed over 74.5k times, collectively.