scholarly journals Emotions of COVID-19: A Study of Self-Reported Information and Emotions during the COVID-19 Pandemic using Human Centric Artificial Intelligence (Preprint)

2021 ◽  
Author(s):  
Achini Adikari ◽  
Rashmika Nawaratne ◽  
Daswin De Silva ◽  
Sajani Ranasinghe ◽  
Oshadi Alahakoon ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Language Processing ◽  
Human Life ◽  
Significant Loss ◽  
State Transitions ◽  
Clustering Methods ◽  
Health Crisis ◽  
Use Of Social Media ◽  
Over Time

BACKGROUND The COVID-19 pandemic has caused a global disruption, starting with a public health emergency, followed by a significant loss of human life and a severe economic and social fallout. As physical distancing regulations were introduced to manage outbreaks, individuals, groups and communities took to social media to express their thoughts and emotions reflecting different behaviours. This has led to increased interaction on social media thereby recording diverse behaviours of people as the pandemic progressed. OBJECTIVE This research aims to explore the human behaviours recorded on the digital traces of social media during the pandemic. The investigation is focused on examining the emotions, emotion state and intensity changes, topical associations and different groups of people using social media conversations that uncover informed insights on behaviours during the COVID-19 pandemic. METHODS Our study explores emotion classifications, intensities, transitions, profiles and alignment to key themes and topics, across the four stages of the pandemic; declaration of a global health crisis, first lockdown, easing of restrictions, and the second lockdown. This study employs a human-centric artificial intelligence (AI) based framework comprising of natural language processing, emotion modelling, unsupervised clustering methods that are collectively used to investigate the social media conversations. The investigation was carried out using 73,000 Twitter conversations related to Australia from January to September 2020. RESULTS The outcomes of this study enabled to analyse and visualise different emotion behaviours and concerns reflected on social media during the COVID-19 pandemic. First, the topic analysis showed the diverse and common concerns people have expressed during the four stages of the pandemic. It was noted that starting from personal level concerns, the concerns expressed over social media has escalated to broader concerns over time. Second, the emotion intensity and emotions state transitions showed that ‘fear’ and ‘sad’ emotions were more prominently expressed at first, however, they transition into ‘anger’ and ‘disgust’ over time. Negative emotions except ‘sad’ were significantly higher (P < .05) in the second lockdown showing increased frustration. Emotion state changes during these stages were visualised to comprehend the change in emotions over time. Third, based on the concerns expressed social media users were categorized into profiles. The profiles in the first lockdown differed from the profiles in the second lockdown showing the shift of concerns as the pandemic progressed. CONCLUSIONS This study showed diverse emotion behaviours and concerns recorded on social media during the COVID-19 pandemic. While this study establishes the use of social media to discover informed insights during a time where physical communication is impossible the outcomes also contribute towards post-pandemic recovery and understanding people’s emotions better during crises. The study exploits AI and social media to enhance our understanding of human behaviours in global emergencies, leading to improved planning and policymaking for future crises.

scholarly journals A SEGMENT STUDY OF MORAL CRISIS ON SOCIAL MEDIA AND ONLINE USING THE ARTIFICIAL INTELLIGENCE APPLICATION: A WAKE-UP CALL

2021 ◽  
Vol 6 (22) ◽  
pp. 36-44
Author(s):  
Nor ‘Adha Ab Hamid ◽  
Azizah Mat Rashid ◽  
Mohd Farok Mat Nor
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Criminal Behavior ◽  
Human Life ◽  
Personal Data ◽  
Human Beings ◽  
Legal Provisions ◽  
Qualitative Approaches ◽  
Moral Crisis ◽  
Use Of Social Media

The development of science and technology is always ahead and has no point and seems limitless. Although human beings are the agents who started this development but eventually faced with a bitter situation which can sacrifice human moral, right and interest of our future. Shariah criminal offenses nowadays can not only occur or be witnessed by a person in a meeting physically with the perpetrator. As a result of technological developments, such behavior can occur and can be witnessed in general by larger groups. Although the illegal treatment which is not in accordance with sharia law and the moral crisis issues happening surrounding us and is rampant on social media, no enforcement is done on perpetrators who use social media medium. According to sharia principles, something that is wrong should be prevented and it is the responsibility of all Muslim individuals. But what is happening today, some Shariah criminal behavior, especially in relation to ethics, can occur easily using facilities technology driven by technological ingenuity. If the application of existing legal provisions is limited and has obstacles for enforcement purposes, then the problem needs to be overcome due to development the law should be in line with current developments. The study aims to identify a segment and cases of the moral crisis on social media and online using the artificial intelligence (AI) application and to identify the needs for shariah prevention. This thesis uses qualitative approaches, adopts library-based research, and, by content analysis of documents, applies the literature review approach. The findings show that the use of social media and AI technology has had an impact on various issues such as moral crisis, security, misuse, an intrusion of personal data, and the construction of AI beyond human control. Thus, the involvement and cooperation of various parties are needed in regulating and addressing issues that arise as a result of the use of social media and AI technology in human life.

Fuentes informativas en tiempos de Covid-19: Cómo los medios en Chile narraron la pandemia a través de sus redes sociales

2021 ◽  
Author(s):  
Claudia Mellado ◽  
Luis Cárcamo-Ulloa ◽  
Amaranta Alfaro ◽  
Daria Inai ◽  
José Isbej
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Redes Sociales ◽  
Health Crisis ◽  
Political Sources ◽  
Social Media Platforms ◽  
The Media ◽  
Ma Process ◽  
Use Of Social Media ◽  
Over Time

This study analyzes the use of social media sources by nine news outlets in Chile in regard to Covid-19. We identified the most frequently used types of sources, their evolution over time, and the differences between the various social media platforms used by the Chilean media during the pandemic. Specifically, we extracted 838,618 messages published by Chilean media on Facebook, Instagram, and Twitter between January and December 2020. An initial machine learning (MA) process was applied to automatically identify 168,250 messages that included keywords that link their content to Covid-19. Based on a list of 2,130 entities, another MA process was used to apply a set of rules based on the appearance of declarative verbs or common expressions used by the media when citing a source, and the use of colons or quotation marks to detect the presence of different types of sources in the news content. The results reveal that Chilean media outlets’ use of different voices on social media broadly favored political sources followed by health, citizen, academic-scientific, and economic ones. Although the hierarchy of the most important sources used to narrate the public health crisis tended to remain stable, there were nuances over time, and its variation depended on key historic milestones. An analysis of the use of sources by each platform revealed that Twitter was the least pluralist, giving space to a more restricted group of voices and intensifying the presence of political sources over the others, particularly citizen sources. Finally, our study revealed significant differences across media types in the use of political, health, and citizen sources, with television showing a greater presence than in other types of media. Resumen Se analiza el uso de fuentes en redes sociales de nueve medios de información de referencia en Chile frente al Covid-19. Se identificaron los tipos de fuentes más utilizados, su evolución en el tiempo, así como las diferencias encontradas entre distintas plataformas de redes sociales de los medios chilenos. Específicamente, se extrajeron 838.618 publicaciones de medios nacionales desde Facebook, Instagram y Twitter entre enero y diciembre de 2020. A ese corpus se aplicó un primer proceso de machine learning (MA) para filtrar automáticamente 168.250 publicaciones que incluían palabras claves que identifican su contenido con el Covid-19. A partir de una lista de 2.130 entidades, se utilizó otro proceso de MA para aplicar un conjunto de reglas basadas en la presencia de verbos declarativos o de expresiones comunes usadas por los medios cuando se cita a una entidad, así como el uso de dos puntos o de comillas, con el objeto de detectar distintos tipos de fuentes en el contenido informativo. Los resultados muestran que el uso que los medios chilenos dieron a distintas voces en sus redes sociales favoreció ampliamente a las fuentes políticas, seguidas por las fuentes de salud, y más desde lejos por las ciudadanas, académico-científicas y económicas. Aunque la jerarquía de las fuentes que se usó para narrar la crisis sanitaria tendió a mantenerse estable, tuvo matices a lo largo del tiempo y su variación dependió de los hitos que marcaron la historia del país. Al analizar el uso de fuentes según plataforma, se observa a Twitter como menos pluralista, dando espacio a un grupo más restringido de voces e intensificando la presencia de las fuentes políticas por sobre las demás; en especial, por sobre las ciudadanas. Finalmente, nuestro estudio reveló diferencias significativas en las fuentes utilizadas por publicaciones de origen televisivo, particularmente en el uso de fuentes políticas, de salud y ciudadanas, las cuales tuvieron una presencia mayor que en los demás tipos de medios

Leveraging Twitter Using Artificial Intelligence to Explore Mental Health Insights in the UK During the COVID-19 Pandemic (Preprint)

2021 ◽  
Author(s):  
Christopher Marshall ◽  
Kate Lanyi ◽  
Rhiannon Green ◽  
Georgie Wilkins ◽  
Fiona Pearson ◽  
...  
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Language Processing ◽  
Mental Health Problems ◽  
Well Being ◽  
Research Activity ◽  
Health Crisis ◽  
Key Topics ◽  
The Uk ◽  
The Impact

BACKGROUND There is increasing need to explore the value of soft-intelligence, leveraged using the latest artificial intelligence (AI) and natural language processing (NLP) techniques, as a source of analysed evidence to support public health research activity and decision-making. OBJECTIVE The aim of this study was to further explore the value of soft-intelligence analysed using AI through a case study, which examined a large collection of UK tweets relating to mental health during the COVID-19 pandemic. METHODS A search strategy comprising a list of terms related to mental health, COVID-19, and lockdown restrictions was developed to prospectively collate relevant tweets via Twitter’s advanced search application programming interface over a 24-week period. We deployed a specialist NLP platform to explore tweet frequency and sentiment across the UK and identify key topics of discussion. A series of keyword filters were used to clean the initial data retrieved and also set up to track specific mental health problems. Qualitative document analysis was carried out to further explore and expand upon the results generated by the NLP platform. All collated tweets were anonymised RESULTS We identified and analysed 286,902 tweets posted from UK user accounts from 23 July 2020 to 6 January 2021. The average sentiment score was 50%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume and sentiment appeared to coincide with key changes to any local and/or national social-distancing measures. Tweets around mental health were polarising, discussed with both positive and negative sentiment. Key topics of consistent discussion over the study period included the impact of the pandemic on people’s mental health (both positively and negatively), fear and anxiety over lockdowns, and anger and mistrust toward the government. CONCLUSIONS Through the primary use of an AI-based NLP platform, we were able to rapidly mine and analyse emerging health-related insights from UK tweets into how the pandemic may be impacting people’s mental health and well-being. This type of real-time analysed evidence could act as a useful intelligence source that agencies, local leaders, and health care decision makers can potentially draw from, particularly during a health crisis.

scholarly journals Anatomy of a Protest: Spatial Information, Social Media, and Urban Space

2020 ◽  
Vol 6 (1) ◽  
pp. 205630511989732
Author(s):  
Alireza Karduni ◽  
Eric Sauda
Keyword(s):  
Social Media ◽  
Public Space ◽  
Urban Space ◽  
Language Processing ◽  
Local Community ◽  
Spatial Information ◽  
Social Media Data ◽  
Public Events ◽  
Use Of Social Media ◽  
Media Data

Black Lives Matter, like many modern movements in the age of information, makes significant use of social media as well as public space to demand justice. In this article, we study the protests in response to the shooting of Keith Lamont Scott by police in Charlotte, North Carolina, on September 2016. Our goal is to measure the significance of urban space within the virtual and physical network of protesters. Using a mixed-methods approach, we identify and study urban space and social media generated by these protests. We conducted interviews with protesters who were among the first to join the Keith Lamont Scott shooting demonstrations. From the interviews, we identify places that were significant in our interviewees’ narratives. Using a combination of natural language processing and social network analysis, we analyze social media data related to the Charlotte protests retrieved from Twitter. We found that social media, local community, and public space work together to organize and motivate protests and that public events such as protests cause a discernible increase in social media activity. Finally, we find that there are two distinct communities who engage social media in different ways; one group involved with social media, local community and urban space, and a second group connected almost exclusively through social media.

scholarly journals New explainability method for BERT-based model in fake news detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mateusz Szczepański ◽  
Marek Pawlicki ◽  
Rafał Kozik ◽  
Michał Choraś
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Language Processing ◽  
High Performance ◽  
Real Life ◽  
Contemporary Society ◽  
Fake News ◽  
Interpretable Model ◽  
Deep Integration ◽  
The Impact

AbstractThe ubiquity of social media and their deep integration in the contemporary society has granted new ways to interact, exchange information, form groups, or earn money—all on a scale never seen before. Those possibilities paired with the widespread popularity contribute to the level of impact that social media display. Unfortunately, the benefits brought by them come at a cost. Social Media can be employed by various entities to spread disinformation—so called ‘Fake News’, either to make a profit or influence the behaviour of the society. To reduce the impact and spread of Fake News, a diverse array of countermeasures were devised. These include linguistic-based approaches, which often utilise Natural Language Processing (NLP) and Deep Learning (DL). However, as the latest advancements in the Artificial Intelligence (AI) domain show, the model’s high performance is no longer enough. The explainability of the system’s decision is equally crucial in real-life scenarios. Therefore, the objective of this paper is to present a novel explainability approach in BERT-based fake news detectors. This approach does not require extensive changes to the system and can be attached as an extension for operating detectors. For this purposes, two Explainable Artificial Intelligence (xAI) techniques, Local Interpretable Model-Agnostic Explanations (LIME) and Anchors, will be used and evaluated on fake news data, i.e., short pieces of text forming tweets or headlines. This focus of this paper is on the explainability approach for fake news detectors, as the detectors themselves were part of previous works of the authors.

scholarly journals The Interplay of Jargon, Motivation, and Fatigue While Processing COVID-19 Crisis Communication Over Time

2021 ◽  
pp. 0261927X2110431
Author(s):  
Hillary C. Shulman ◽  
Olivia M. Bullock ◽  
Elizabeth E. Riggs
Keyword(s):  
Language Processing ◽  
Crisis Communication ◽  
Processing Fluency ◽  
Health Crisis ◽  
Public Health Crisis ◽  
Related Information ◽  
Message Acceptance ◽  
Practical Implications ◽  
Risk Policy ◽  
Over Time

Using the backdrop of the COVID-19 pandemic, this three-wave experiment ( N = 1,830) examined whether a public health crisis motivates people to engage with complicated information about the virus in the form of jargon. Results revealed that although the presence of jargon negatively impacted message acceptance for topics that were not particularly urgent (flood risk and federal risk policy), the presence of jargon within the COVID-19 topic condition did not affect message perceptions—at first. In subsequent waves of data collection, however, it was found that the influence of jargon strengthened over time within the COVID-19 topic condition. Specifically, jargon began to exert a stronger influence on processing fluency despite the continued urgency of the topic. This finding suggests that motivation to process COVID-19 related information declined over time. Theoretical contributions for language, processing fluency, and persuasion are offered and practical implications for health, risk, science, and crisis communicators are advanced.

scholarly journals Role of Artificial Intelligence in Cyber Security

2019 ◽  
Vol 9 (1) ◽  
pp. 4628-4630
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Cyber Security ◽  
Vision System ◽  
Human Life ◽  
News Item ◽  
Security Measures ◽  
Daily News ◽  
Cyber Crimes ◽  
Financial Transactions

Artificial Intelligence (AI) is a buzz word in the cyber world. It is still a developing science in multiple facets according to the challenges thrown by 21st century. Use of AI has become inseparable from human life. In this day and age one cannot imagine a world without AI as it has much significant impact on human life. The main objective of AI is to develop the technology based activities which represents the human knowledge in order to solve problems. Simply AI is study of how an individual think, work, learn and decide in any scenario of life, whether it may be related to problem solving or learning new things or thinking rationally or to arrive at a solution etc. AI is in every area of human life, naming a few it is into gaming, language processing, speech recognition, expert system, vision system, hand writing recognition, intelligence robots, financial transactions and what not, every activity of human life has become a subset of AI. In spite of numerous uses, AI can also used for destroying the human life, that is the reason human inference is required to monitor the AI activities. Cyber crimes has become quite common and become a daily news item. It is not just a problem faced in one country, it is across the world. Without strong security measures, AI is meaningless as it can be easily accessible by others. It has become a big threat for governments, banks, multinational companies through online attacks by hackers. Lot of individual and organizational data is exploited by hackers and it becomes a big threat to the cyber world. In this connection research in the area of AI and cyber security has gained more importance in the recent times and it is ever lasting also as it is a dynamic and sensitive issue linked to human life.

scholarly journals Toxic Comment Classification on Social Media Using Support Vector Machine and Chi Square Feature Selection

2021 ◽  
Vol 7 (1) ◽  
pp. 64-76
Author(s):  
Nadhia Azzahra ◽  
Danang Murdiansyah ◽  
Kemas Lhaksmana
Keyword(s):  
Social Media ◽  
Feature Selection ◽  
Support Vector ◽  
Linear Kernel ◽  
Chi Square ◽  
Svm Model ◽  
Ease Of Access ◽  
Use Of Social Media ◽  
Over Time

The use of social media in society continues to increase over time and the ease of access and familiarity of social media then make it easier for an irresponsible user to do unethical things such as spreading hatred, defamation, radicalism, pornography so on. Although there are regulations that govern all the activities on social media. However, the regulations are still not working effectively. In this study, we conducted a classification of toxic comments containing unethical matters using the SVM method with TF-IDF as the feature extraction and Chi Square as the feature selection. The best performance result based on the experiment that has been carried out is by using the SVM model with a linear kernel, without implementing Chi Square, and using stemming and stopwords removal with the F1 − Score equal to 76.57%.

Strong Artificial Intelligence and Consciousness

2020 ◽  
Vol 07 (01) ◽  
pp. 63-72 ◽  
Author(s):  
Gee Wah Ng ◽  
Wang Chi Leung
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Human Life ◽  
Autonomous Systems ◽  
Holistic Approach ◽  
Future Research ◽  
Strong Ai ◽  
Strong Artificial Intelligence ◽  
Future Research Directions

In the last 10 years, Artificial Intelligence (AI) has seen successes in fields such as natural language processing, computer vision, speech recognition, robotics and autonomous systems. However, these advances are still considered as Narrow AI, i.e. AI built for very specific or constrained applications. These applications have its usefulness in improving the quality of human life; but it is not good enough to do highly general tasks like what the human can do. The holy grail of AI research is to develop Strong AI or Artificial General Intelligence (AGI), which produces human-level intelligence, i.e. the ability to sense, understand, reason, learn and act in dynamic environments. Strong AI is more than just a composition of Narrow AI technologies. We proposed that it has to be a holistic approach towards understanding and reacting to the operating environment and decision-making process. The Strong AI must be able to demonstrate sentience, emotional intelligence, imagination, effective command of other machines or robots, and self-referring and self-reflecting qualities. This paper will give an overview of current Narrow AI capabilities, present the technical gaps, and highlight future research directions for Strong AI. Could Strong AI become conscious? We provide some discussion pointers.

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