Michal Monselise’s research while affiliated with Drexel University and other places

Aggressive driving is known to be a cause of vehicle accidents. Individuals with Attention-deficit hyperactivity disorder (ADHD) are prone to more aggressive behavior and that also leads to aggressive driving. To prevent aggressive driving, we strive to first understand aggressive driving and find patterns in this type of driving behavior. In an effort to uncover to identify patterns in aggressive driving, we examine sensor data and video data of trips taken by drivers with ADHD and identify our distinct aggressive driving patterns. Using the sensor data, we extend our findings to all aggressive trips in our dataset and generate a model to detect aggressive driving patterns. By finding the similarity between trips and then using these distances to produce a KNN model, we are able to model our data and classify it into 4 driving patterns. This analysis can better inform us of the type of driving patterns that appear in aggressive driving. Using this analysis, we can also better understand which patterns are produce better precision and recall using this methodology.

Knowledge Organization Systems (KOS) as networks of knowledge have the potential to inform AI operations. This paper explores natural language processing and machine learning in the context of KOS and Helping Interdisciplinary Vocabulary Engineering (HIVE) technology. The paper presents three use cases: HIVE and Historical Knowledge Networks, HIVE for Materials Science (HIVE-4-MAT), and Using HIVE to Enhance and Explore Medical Ontologies. The background section reviews AI foundations, while the use cases provide a frame of reference for discussing current progress and implications of connecting KOS to AI in digital resource collections.

Background: As a number of vaccines for COVID-19 are given emergency use authorization by local health agencies and are being administered in multiple countries, it is crucial to gain public trust in these vaccines to ensure herd immunity through vaccination. One way to gauge public sentiment regarding the vaccines for the goal of increasing vaccination rates is by analyzing social media such as Twitter. Objective: The goal of this research is to understand public sentiment towards COVID-19 vaccines by analyzing discussions about the vaccines on social media for a period of sixty days when the vaccines were started in US. Using the combination of topic detection and sentiment analysis, we identify different types of concerns regarding vaccines that are expressed by different groups of the public that appear in social media. Methods: To better understand public sentiment, we collected tweets for exactly 60 days starting December 16th, 2020 that contained hashtags or keywords related to COVID-19 vaccines. We detected and analyzed the different topics of discussion of these tweets as well as their emotional content. Vaccine topics were identified using non-negative matrix factorization (NMF) and emotional content was identified using the VADER sentiment analysis library as well as using sentence BERT embeddings and comparing the embedding to different emotions using cosine similarity. Results: After removing all duplicates and retweets, 7,864,640 were collected during the time period. Topic modeling resulted in 50 topics of those we selected the 12 topics with the highest volume of tweets for analysis. Administration and access to vaccines are some of the major concerns in the pubic. Additionally, we classified the tweets in each topic into one of 5 emotions and found fear to be the leading emotion in the tweets followed by joy. Conclusions: This research focuses not only on negative emotions that may lead to vaccine hesitancy but also on positive emotions toward the vaccine. By identifying both positive and negative emotions, we are able to identify the public's response to the vaccines overall and to news events related to the vaccines. These results are useful in developing plans for disseminating the authoritative health information and better communication to build understanding and trust.

BACKGROUND As a number of vaccines for COVID-19 are given emergency use authorization by local health agencies and are being administered in multiple countries, it is crucial to gain public trust in these vaccines to ensure herd immunity through vaccination. One way to gauge public sentiment regarding vaccines for the goal of increasing vaccination rates is by analyzing social media such as Twitter. OBJECTIVE The goal of this research was to understand public sentiment toward COVID-19 vaccines by analyzing discussions about the vaccines on social media for a period of 60 days when the vaccines were started in the United States. Using the combination of topic detection and sentiment analysis, we identified different types of concerns regarding vaccines that were expressed by different groups of the public on social media. METHODS To better understand public sentiment, we collected tweets for exactly 60 days starting from December 16, 2020 that contained hashtags or keywords related to COVID-19 vaccines. We detected and analyzed different topics of discussion of these tweets as well as their emotional content. Vaccine topics were identified by nonnegative matrix factorization, and emotional content was identified using the Valence Aware Dictionary and sEntiment Reasoner sentiment analysis library as well as by using sentence bidirectional encoder representations from transformer embeddings and comparing the embedding to different emotions using cosine similarity. RESULTS After removing all duplicates and retweets, 7,948,886 tweets were collected during the 60-day time period. Topic modeling resulted in 50 topics; of those, we selected 12 topics with the highest volume of tweets for analysis. Administration and access to vaccines were some of the major concerns of the public. Additionally, we classified the tweets in each topic into 1 of the 5 emotions and found fear to be the leading emotion in the tweets, followed by joy. CONCLUSIONS This research focused not only on negative emotions that may have led to vaccine hesitancy but also on positive emotions toward the vaccine. By identifying both positive and negative emotions, we were able to identify the public's response to the vaccines overall and to news events related to the vaccines. These results are useful for developing plans for disseminating authoritative health information and for better communication to build understanding and trust.

With the novel coronavirus (COVID-19) pandemic affecting the lives of the citizens of over 200 countries, there is a need for policy makers and clinicians to understand public sentiment and track the spread of the disease. One of the sources for gaining valuable insight into public sentiment is through social media. This study aims to extract this insight by producing a list of the most discussed topics regarding COVID-19 on Twitter every week and monitoring the evolution of topics from week to week. This research will propose two topic mining that can handle a large-scale dataset—rolling online non-negative matrix factorization (Rolling-ONMF) and sliding online non-negative matrix factorization (Sliding-ONMF)—and compare the insights produced by both techniques. Each algorithm produces 425 topics over the course of 17 weeks. However, topics that have not evolved from one week to the next beyond a certain evolution threshold are consolidated into a single topic. Since the topics produced by the Rolling-ONMF algorithm each week depend on the topics from the previous week, we find that the Sliding-ONMF algorithm produces more varied topics each week; however, the topics produced by the Rolling-ONMF algorithm contain keywords that appear more consistent with each other when reviewing the terms manually. We also observe that the Sliding-ONMF algorithm is able to capture events that have shorter time frames rather than ones that last throughout many months while the Rolling-ONMF algorithm detects more general themes due to a higher average evolution score which leads to more topic consolidation. We have also conducted a qualitative analysis and grouped the detected topics into themes. A number of important themes such as government policy, economic crisis, COVID-19-related updates, COVID-19-related events, prevention, vaccines and treatments, and COVID-19 testing are identified. These reflected the concerns related to the pandemic expressed in social media.