Influence of tweet features on emoticon usage

Twitter, a social media created 13 years ago is a platform which provides users to express their views regarding numerous issues. Reportedly, Twitter currently has 321 million users from all around the world and produces about 200 million tweets in a day. There is a large collection of data available that enables performance of analysis to solve the real-world issues. The usage of emoticons is rapidly growing as they provide higher impressibility and feasibility. In late 90's, emoticons were simple representations, like {":)" ":( "":|" ":*" } etc. After Unicode consortium standardization, all the leading social media and mobile developers came up with improved versions of emoticons. This research involves analyzing the emoticon usage patterns in topics to identify the influence of multiple features such as age and location on the utilization of emoticons. Correlation of tweets and emoticon count is evaluated

An Analysis of Selected Clusters of Fires in Florida (1996 - 2018)

Hidden within the day-to-day routine responses of fire departments are possible multiple fires set by individuals in a small area with an unusual frequency. These fires may be the initial announcement of an emerging serial offender who is declaring a wound, an intolerable life situation, or is triggered by unknown events. This study sought to predict incendiary fires within a known cluster by using observable variables, both prior to and after the cluster event. This was done to further our understanding of the characteristics of clusters of intentionally lit fires. This was accomplished by examining the Florida Fire Information Reports (FFIRS) from the Florida State Fire Marshal’s Division and the Augmented Criminal Information (ACISS) reports provided by the Florida Division of Investigative and Forensic Services, Bureau of Fire, Arson and Explosives Investigation from 1996 to 2018. The study surveyed 1,260,369 records from which 45 clusters comprised of 1216 individual fire events were used

Data Science and Knowledge Discovery

Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining

Religious Violence and Twitter: Networks of Knowledge, Empathy and Fascination

Twitter analysis through data mining, text analysis, and visualization, coupled with the application of actor-network-theory, reveals a coalition of heterogenous religious affiliations around grief and fascination. While religious violence has always existed, the prevalence of social media has led to an increase in the magnitude of discussions around the topic. This paper examines the different reactions on Twitter to violence targeting three religious communities: the 2015 Charleston Church shooting, the 2018 Pittsburgh Synagogue shooting, and the 2019 Christchurch Mosque shootings. The attacks were all perpetrated by white nationalists with firearms. By analyzing large Twitter datasets in response to the attacks, we were able to render visible associations among actors across religions communities, national identities, and political persuasions. What this project revealed is that if we apply actor-network-theory and data visualization to look at networks created by human/non-human (text, computer, phone, meme, tweet, retweet, hashtag) actors, we can see that knowledge, empathy, and fascination drive communication around mass violence against religious communities

Neurological and Mental Disorders

Mental disorders can result from disruption of neuronal circuitry, damage to the neuronal and non-neuronal cells, altered circuitry in the different regions of the brain and any changes in the permeability of the blood brain barrier. Early identification of these impairments through investigative means could help to improve the outcome for many brain and behaviour disease states.The chapters in this book describe how these abnormalities can lead to neurological and mental diseases such as ADHD (Attention Deficit Hyperactivity Disorder), anxiety disorders, Alzheimer’s disease and personality and eating disorders. Psycho-social traumas, especially during childhood, increase the incidence of amnesia and transient global amnesia, leading to the temporary inability to create new memories.Early detection of these disorders could benefit many complex diseases such as schizophrenia and depression

Computation in Complex Networks

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin

Ecological Influences on Dietary Behavior: The Interaction Between Person and Neighborhood Environment in a Low-SES, Hispanic Community

Despite existing knowledge about lifestyle choices and their relationships to obesity and diabetes, the prevalence of Type 2 Diabetes Mellitus (T2DM) is increasing, and the number of Americans at risk is greater than 70% (CDC, 2019). Obesity is a complex, multifactorial, and largely preventable disease, affecting, along with overweight, over a third of the world\u27s population today. A significant factor in lifestyle management is the ecology of food choice. Literature suggests that environment and exposure can predict food-related health risk behaviors and health outcomes. The objective of this dissertation was to conceptualize and carry out a series of pilot studies relative to the refinement of nutrition ecological issues, methodologies, and measures. After a review of methodological difficulties, gaps, and unresolved issues, I propose methodological solutions, present the methodologies and results of pilot studies about the feasibility of these solutions. Study one involved database and windshield survey of more than 200 retail food locations in Jurupa Valley, CA. After redefining criteria for good quality food providers, this area was found to have an abundance of fast food and convenience stores and limited access to stores of the best nutritional quality. Study two included adaptations and implementation of a direct measure of the nutrition environment in Pomona, CA. The target area was a high-risk corridor with a concentration of both community activity and retail locations. Using database analysis and visual assessment, a list of stores was compiled, and of the 91 stores in the target area, 60 were surveyed for overall scores, which was a sum score of quality, accessibility, availability of fresh fruits and vegetables, and price. The results indicated that Grocery, Independent Market, and Ethnic Food outlets could be considered together as high-quality or at least higher quality in that they provided overall, price, access, and quality scores in the modified NEMS survey that were not statistically different from one another in quality of offerings (Kruskal-Wallis H X2(2) .386-6.726, p=. 035 to .832 (only significant value was for availability of fresh fruits and vegetables). Furthermore, all sub-types of convenience stores, including independent locations, those associated with a gas station, and liquor stores can be considered together, Kruskal-Wallis test X2(1.788-5.535) p= .63 to .409 (near-significant values for Price and Quality). Study three presents a methodology for accurately assessing the retail food environment using walking surveys on a GIS enabled mapping application. Results revealed significant inconsistencies between database (GIS) data and survey data acquired from current observations of the actual locations in the community; there was a 31% error in database findings. Additional comparisons were made between GIS results and participant data, which indicated possible patterns of positive or negative health and intake outcomes with neighborhood retail food availability. Results from this series of developmental studies indicated a need for primary data sources whenever possible for compiling information about retail food locations. As well, the methodology for collecting business-types from databases and for completing a safe and thorough environmental scan for retail food locations was presented in this dissertation. Additional findings indicated that a refined methodology to score store quality identified considerable variation between store types. These results may have implications for city planning, diabetes prevention, and lifestyle management programs