User Centered Design

Social media (and the world at large) have been awash with news of the COVID-19 pandemic With the passage of time, news and awareness about COVID-19 spread like the pandemic itself, with an explosion of messages, updates, videos, and posts Mass hysteria manifest as another concern in addition to the health risk that COVID-19 presented Predictably, public panic soon followed, mostly due to misconceptions, a lack of information, or sometimes outright misinformation about COVID-19 and its impacts It is thus timely and important to conduct an ex post facto assessment of the early information flows during the pandemic on social media, as well as a case study of evolving public opinion on social media which is of general interest This study aims to inform policy that can be applied to social media platforms; for example, determining what degree of moderation is necessary to curtail misinformation on social media This study also analyzes views concerning COVID-19 by focusing on people who interact and share social media on Twitter As a platform for our experiments, we present a new large-scale sentiment data set COVIDSENTI, which consists of 90 000 COVID-19-related tweets collected in the early stages of the pandemic, from February to March 2020 The tweets have been labeled into positive, negative, and neutral sentiment classes We analyzed the collected tweets for sentiment classification using different sets of features and classifiers Negative opinion played an important role in conditioning public sentiment, for instance, we observed that people favored lockdown earlier in the pandemic; however, as expected, sentiment shifted by mid-March Our study supports the view that there is a need to develop a proactive and agile public health presence to combat the spread of negative sentiment on social media following a pandemic

/pdf/covidsenti-a-large-scale-benchmark-twitter-data-set-for-1ba4eelkpi.pdf

COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis

Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19’s informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naive Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

https://www.mdpi.com/2078-2489/11/6/314/pdf

COVID-19 public sentiment insights and machine learning for tweets classification

Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19's informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naive Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

/pdf/covid-19-public-sentiment-insights-and-machine-learning-for-1w0a28v3st.pdf

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

https://medrxiv.org/content/10.1101/2020.06.01.20119347v1.full.pdf

Personality Assessment using Twitter Tweets

Usability evaluation of adaptive features in smartphones

All applications are developed for context adaptation and provide communication with users through their interfaces. These applications offer new opportunities for developers as well as users by collecting context data and adapting systems behavior accordingly. Particularly, in mobile devices, these mechanisms provide usability increment tremendously. Rigid and non-adaptive interface blocks the features of context awareness. In this paper, we study methods, technologies and criteria which have been proposed specifically for adaptive interfaces. Based on these guidelines, we elaborate the intelligence of adaptivity and usage of context according to user mental model. Further, we have proposed a model to develop user context ontology (UCO) and adaptive interface ontology (AIO) to optimize the use of adaptive mobile interfaces in the context of user preferences. These ontologies organize the perceptions and thoughts of user. The philosophy of User Centered Design (UCD) is proposed to analyze the usability and validity of mobile device interfaces according to user contexts.

https://thesai.org/Downloads/Volume9No11/Paper_77-Towards_Adaptive_User_Interfaces_for_Mobile_Phone.pdf

Towards Adaptive user Interfaces for Mobile-Phone in Smart World

There is a growing interest in developing user interfaces of self-driving cars or automated vehicles which are also known as driverless cars. The literature on Self-driving cars has steadily been growing. However, there has not been any effort to systematically select, review, and synthesize the literature on this topic. A systematic literature review is conducted that reports different perspectives of autonomous cars like liabilities and responsibilities of these Self-driving cars in case of accidents, ethics related to decision making, potentials and drawbacks of using Self-driving cars, people's perspective about them and challenges faced by them. Data has been extracted from different papers to identify various aspects of self-driving cars as discussed above. More recent and current findings are considered to deal with these research issues. Review's and findings presented in this paper are expected to help researchers and practitioners to understand the Self-driving cars from various perspectives.

Systematic literature review on user interfaces of autonomous cars: Liabilities and responsibilities

Background The reduction and control over neonatal, infant, and maternal mortality is a collective mission of the World Health Organization under United Nations. Methods This article summarizes the automation of verbal autopsy reporting for neonatal, infant, and maternal mortality with primary focus on user-centered design for technologically illiterate workforce with minimum available resources. The diminution in neonatal, infant, and maternal deaths is not possible until grassroot level quality data are available for mortality. The estimated data are less effective for developing countries like Pakistan because it has heterogeneous demographic pockets with respect to mortality causes. The Neonatal, Infant, and Maternal Death E-surveillance System is a project in which a real-time reporting system is innovated that is useful in detecting the causes of mortality and effective in adopting appropriate countermeasure policies. In a pilot study, the system was implemented initially in nine districts of Punjab, Pakistan. The initial system was refined after getting detailed feedback from district management staff including Lady Health Workers and Lady Health Supervisors. The refined surveillance system was finally implemented in all 36 districts of Punjab, Pakistan. Results The results exhibited 31% improvement in infant data collection and 6% improvement in maternal data collection regarding mortality. Conclusion This research will be helpful in achieving the milestone of gathering real-time mortality data from grassroot level using user-centered design methodology.

/pdf/a-health-informatics-reporting-system-for-technology-21rr0ukoka.pdf

Nadeem Ahmad

Papers

Personality Assessment using Twitter Tweets

Usability evaluation of adaptive features in smartphones

Towards Adaptive user Interfaces for Mobile-Phone in Smart World

Systematic literature review on user interfaces of autonomous cars: Liabilities and responsibilities

A Health Informatics Reporting System for Technology Illiterate Workforce Using Mobile Phone.