Visualization, Spatiotemporal Patterns, and Directional Analysis of Urban Activities Using Geolocation Data Extracted from LBSN

2020 ◽  
Vol 9 (2) ◽  
pp. 137 ◽  
Author(s):  
Muhammad Rizwan ◽  
Wanggen Wan ◽  
Luc Gwiazdzinski
Keyword(s):  
Social Media ◽  
Movement Patterns ◽  
District Level ◽  
Smart Devices ◽  
Daily Routine ◽  
Space And Time ◽  
Directional Analysis ◽  
Wide Range ◽  
And Behavior ◽  
Urban Activities

Location-based social networks (LBSNs) have rapidly prevailed in China with the increase in smart devices use, which has provided a wide range of opportunities to analyze urban behavior in terms of the use of LBSNs. In a LBSN, users socialize by sharing their location (also referred to as “geolocation”) in the form of a tweet (also referred to as a “check-in”), which contains information in the form of, but is not limited to, text, audio, video, etc., which records the visited place, movement patterns, and activities performed (e.g., eating, living, working, or leisure). Understanding the user’s activities and behavior in space and time using LBSN datasets can be achieved by archiving the daily activities, movement patterns, and social media behavior patterns, thus representing the user’s daily routine. The current research observing and analyzing urban activities behavior was often supported by the volunteered sharing of geolocation and the activity performed in space and time. The objective of this research was to observe the spatiotemporal and directional trends and the distribution differences of urban activities at the city and district levels using LBSN data. The density was estimated, and the spatiotemporal trend of activities was observed, using kernel density estimation (KDE); for spatial regression analysis, geographically weighted regression (GWR) analysis was used to observe the relationship between different activities in the study area. Finally, for the directional analysis, to observe the principle orientation and direction, and the spatiotemporal movement and extension trends, a standard deviational ellipse (SDE) analysis was used. The results of the study show that women were more inclined to use social media compared with men. However, the activities of male users were different during weekdays and weekends compared to those of female users. The results of the directional analysis at the district level reflect the change in the trajectory and spatiotemporal dynamics of activities. The directional analysis at the district level reveals its fine spatial structure in comparison to the whole city level. Therefore, LBSN can be considered as a supplementary and reliable source of social media big data for observing urban activities and behavior within a city in space and time.

Building High Performance Explainable Machine Learning Models for Social Media-based Substance Use Prediction

2020 ◽  
Vol 29 (03n04) ◽  
pp. 2060009
Author(s):  
Tao Ding ◽  
Fatema Hasan ◽  
Warren K. Bickel ◽  
Shimei Pan
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Substance Use ◽  
Human Behavior ◽  
High Performance ◽  
Supervised Machine Learning ◽  
Learning Models ◽  
Wide Range ◽  
And Behavior ◽  
Machine Learning Models

Social media contain rich information that can be used to help understand human mind and behavior. Social media data, however, are mostly unstructured (e.g., text and image) and a large number of features may be needed to represent them (e.g., we may need millions of unigrams to represent social media texts). Moreover, accurately assessing human behavior is often difficult (e.g., assessing addiction may require medical diagnosis). As a result, the ground truth data needed to train a supervised human behavior model are often difficult to obtain at a large scale. To avoid overfitting, many state-of-the-art behavior models employ sophisticated unsupervised or self-supervised machine learning methods to leverage a large amount of unsupervised data for both feature learning and dimension reduction. Unfortunately, despite their high performance, these advanced machine learning models often rely on latent features that are hard to explain. Since understanding the knowledge captured in these models is important to behavior scientists and public health providers, we explore new methods to build machine learning models that are not only accurate but also interpretable. We evaluate the effectiveness of the proposed methods in predicting Substance Use Disorders (SUD). We believe the methods we proposed are general and applicable to a wide range of data-driven human trait and behavior analysis applications.

scholarly journals Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

2019 ◽  
Vol 11 (10) ◽  
pp. 2822 ◽  
Author(s):  
Rizwan Muhammad ◽  
Yaolong Zhao ◽  
Fan Liu
Keyword(s):  
Social Media ◽  
Social Network ◽  
Activity Patterns ◽  
Low Frequency ◽  
Spatiotemporal Analysis ◽  
Geographic Area ◽  
Primary Objective ◽  
Space And Time ◽  
Wide Range ◽  
Location Based Social Network

In a location-based social network, users socialize with each other by sharing their current location in the form of “check-in,” which allows users to reveal the current places they visit as part of their social interaction. Understanding this human check-in phenomenon in space and time on location based social network (LBSN) datasets, which is also called “check-in behavior,” can archive the day-to-day activity patterns, usage behaviors toward social media, and presents spatiotemporal evidence of users’ daily routines. It also provides a wide range of opportunities to observe (i.e., mobility, urban activities, defining city boundary, and community problems in a city). In representing human check-in behavior, these LBSN datasets do not reflect the real-world events due to certain statistical biases (i.e., gender prejudice, a low frequency in sampling, and location type prejudice). However, LBSN data is primarily considered a supplement to traditional data sources (i.e., survey, census) and can be used to observe human check-in behavior within a city. Different interpretations are used elusively for the term “check-in behavior,” which makes it difficult to identify studies on human check-in behavior based on LBSN using the Weibo dataset. The primary objective of this research is to explore human check-in behavior by male and female users in Guangzhou, China toward using Chinese microblog Sina Weibo (referred to as “Weibo”), which is missing in the existing literature. Kernel density estimation (KDE) is utilized to explore the spatiotemporal distribution geographically and weighted regression (GWR) method was applied to observe the relationship between check-in and districts with a focus on gender during weekdays and weekend. Lastly, the standard deviational ellipse (SDE) analysis is used to systematically analyze the orientation, direction, spatiotemporal expansion trends and the differences in check-in distribution in Guangzhou, China. The results of this study show that LBSN is a reliable source of data to observe human check-in behavior in space and time within a specified geographic area. Furthermore, it shows that female users are more likely to use social media as compared to male users. The human check-in behavior patterns for social media network usage by gender seems to be slightly different during weekdays and weekend.

RepPer: Perception of Psychiatric Disorders on Twitter in French (Preprint)

2020 ◽  
Author(s):  
Sarah Delanys ◽  
Farah Benamara ◽  
Véronique Moriceau ◽  
François Olivier ◽  
Josiane Mothe
Keyword(s):  
Social Media ◽  
Psychiatric Disorders ◽  
Digital Technology ◽  
Psychotic Disorders ◽  
Negative Polarity ◽  
Machine Learning Algorithms ◽  
Annotation Scheme ◽  
Word Use ◽  
Wide Range ◽  
Initial Dataset

BACKGROUND With the advent of digital technology and specifically user generated contents in social media, new ways emerged for studying possible stigma of people in relation with mental health. Several pieces of work studied the discourse conveyed about psychiatric pathologies on Twitter considering mostly tweets in English and a limited number of psychiatric disorders terms. This paper proposes the first study to analyze the use of a wide range of psychiatric terms in tweets in French. OBJECTIVE Our aim is to study how generic, nosographic and therapeutic psychiatric terms are used on Twitter in French. More specifically, our study has three complementary goals: (1) to analyze the types of psychiatric word use namely medical, misuse, irrelevant, (2) to analyze the polarity conveyed in the tweets that use these terms (positive/negative/neural), and (3) to compare the frequency of these terms to those observed in related work (mainly in English ). METHODS Our study has been conducted on a corpus of tweets in French posted between 01/01/2016 to 12/31/2018 and collected using dedicated keywords. The corpus has been manually annotated by clinical psychiatrists following a multilayer annotation scheme that includes the type of word use and the opinion orientation of the tweet. Two analysis have been performed. First a qualitative analysis to measure the reliability of the produced manual annotation, then a quantitative analysis considering mainly term frequency in each layer and exploring the interactions between them. RESULTS One of the first result is a resource as an annotated dataset . The initial dataset is composed of 22,579 tweets in French containing at least one of the selected psychiatric terms. From this set, experts in psychiatry randomly annotated 3,040 tweets that corresponds to the resource resulting from our work. The second result is the analysis of the annotations; it shows that terms are misused in 45.3% of the tweets and that their associated polarity is negative in 86.2% of the cases. When considering the three types of term use, 59.5% of the tweets are associated to a negative polarity. Misused terms related to psychotic disorders (55.5%) are more frequent to those related to mood disorders (26.5%). CONCLUSIONS Some psychiatric terms are misused in the corpora we studied; which is consistent with the results reported in related work in other languages. Thanks to the great diversity of studied terms, this work highlighted a disparity in the representations and ways of using psychiatric terms. Moreover, our study is important to help psychiatrists to be aware of the term use in new communication media such as social networks which are widely used. This study has the huge advantage to be reproducible thanks to the framework and guidelines we produced; so that the study could be renewed in order to analyze the evolution of term usage. While the newly build dataset is a valuable resource for other analytical studies, it could also serve to train machine learning algorithms to automatically identify stigma in social media.

Understanding Distressed Residential Transaction Discounts Across Price Segments and Market Conditions

2020 ◽  
Vol 42 (1) ◽  
pp. 151-182
Author(s):  
Ramya Rajajagadeesan Aroul ◽  
J. Andrew Hansz ◽  
Mauricio Rodriguez
Keyword(s):  
Transaction Costs ◽  
Time Varying ◽  
Market Segment ◽  
Market Conditions ◽  
Large Market ◽  
Space And Time ◽  
Wide Range ◽  
Time Periods ◽  
Market Areas

In the literature, there is a wide range of discounts associated with foreclosures. Comparisons across studies are difficult as they use different methodologies across large areas over different time periods. We employ a consistent methodology across space and time. We find modest discounts, within the range of typical transaction costs, in all but the highest priced market segment. Higher priced segments could explain prior findings of substantial discounts. We find that discounts are time-varying, with discounts increasing with market distress. A one-size-fits-all approach is not appropriate when estimating distressed transaction discounts across large market areas or under changing market conditions.

Tickets for the Inclusive Museum

2020 ◽  
pp. 390-406
Author(s):  
Magda Nikolaraizi ◽  
Charikleia Kanari ◽  
Marc Marschark
Keyword(s):  
Hard Of Hearing ◽  
Learning Experiences ◽  
Learning Needs ◽  
Participatory Learning ◽  
Maximum Benefit ◽  
Wide Range ◽  
The Right ◽  
Physical Features ◽  
And Behavior

In recent years, museums of various kinds have broadened their mission and made systematic efforts to develop a dynamic role in learning by offering a wide range of less formal experiences for individuals with diverse characteristics, including individuals who are deaf or hard-of-hearing (DHH). Despite the worthwhile efforts, in the case of DHH individuals, museums frequently neglect to consider their unique communication, cognitive, cultural, and learning characteristics, thus limiting their access and opportunities for fully experiencing what museums have to offer. This chapter examines the potential for creating accessible museum environments and methods that reflect an understanding of the diverse communication, cognitive, cultural, and learning needs of DHH visitors, all of which enhance their access and participation in the museum activities. The role of the physical features of museum spaces for the access and behavior of DHH visitors is emphasized, together with attention to exhibition methods and the communication and cognitive challenges that need to be considered so DHH visitors can get the maximum benefit. The chapter emphasizes the right of individuals who are DHH to nonformal learning and analyzes how museums could become more accessible to DHH individuals by designing, from the beginning, participatory learning experiences that address their diverse needs.

scholarly journals Young adults’ coping strategies against loneliness during the COVID-19-related quarantine in Greece

2021 ◽  
Author(s):  
Adrianos Golemis ◽  
Panteleimon Voitsidis ◽  
Eleni Parlapani ◽  
Vasiliki A Nikopoulou ◽  
Virginia Tsipropoulou ◽  
...  
Keyword(s):  
Social Media ◽  
Young Adults ◽  
Social Responsibility ◽  
Coping Strategies ◽  
Online Survey ◽  
Adult Population ◽  
Psychological Impact ◽  
Well Being ◽  
Daily Routine ◽  
Cross Sectional

Summary COVID-19 and the related quarantine disrupted young adults’ academic and professional life, daily routine and socio-emotional well-being. This cross-sectional study focused on the emotional and behavioural responses of a young adult population during the COVID-19-related quarantine in April 2020, in Greece. The study was conducted through an online survey. A total of 1559 young adults, aged 18−30 years, completed Steele’s Social Responsibility Motivation Scale and the De Jong Gierveld Loneliness Scale, and answered questions about compliance with instructions, quarantine-related behaviours and coping strategies. According to the results, participants displayed a relatively high sense of social responsibility (M = 16.09, SD = 2.13) and a trend towards moderate feeling of loneliness (M = 2.65, SD = 1.62); young women reported significantly higher levels of loneliness than men. The majority complied with instructions often (46.4%) or always (44.8%). Significantly more women created a new social media account and used the social media longer than 5 h/day, compared with men. Resorting to religion, practicing sports and sharing thoughts and feelings about COVID-19 with others predicted higher levels of social responsibility; humour, practicing sports and sharing thoughts and feelings about COVID-19 with others predicted lower levels of loneliness. Conclusively, COVID-19 is expected to have a significant psychological impact on young adults. Currently, Greece is going through the second quarantine period. This study raises awareness about loneliness in young adults during the COVID-19-related quarantine and highlights the importance of developing online programmes, attractive to younger people, to nurture adaptive coping strategies against loneliness.

scholarly journals Drivers of Infectious Disease Seasonality: Potential Implications for COVID-19

2021 ◽  
pp. 074873042098732
Author(s):  
N. Kronfeld-Schor ◽  
T. J. Stevenson ◽  
S. Nickbakhsh ◽  
E. S. Schernhammer ◽  
X. C. Dopico ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Multidisciplinary Approach ◽  
Preliminary Assessment ◽  
Epidemiological Evidence ◽  
Wide Range ◽  
Human Coronaviruses ◽  
Induced Changes ◽  
And Behavior ◽  
Timing Phase

Not 1 year has passed since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19). Since its emergence, great uncertainty has surrounded the potential for COVID-19 to establish as a seasonally recurrent disease. Many infectious diseases, including endemic human coronaviruses, vary across the year. They show a wide range of seasonal waveforms, timing (phase), and amplitudes, which differ depending on the geographical region. Drivers of such patterns are predominantly studied from an epidemiological perspective with a focus on weather and behavior, but complementary insights emerge from physiological studies of seasonality in animals, including humans. Thus, we take a multidisciplinary approach to integrate knowledge from usually distinct fields. First, we review epidemiological evidence of environmental and behavioral drivers of infectious disease seasonality. Subsequently, we take a chronobiological perspective and discuss within-host changes that may affect susceptibility, morbidity, and mortality from infectious diseases. Based on photoperiodic, circannual, and comparative human data, we not only identify promising future avenues but also highlight the need for further studies in animal models. Our preliminary assessment is that host immune seasonality warrants evaluation alongside weather and human behavior as factors that may contribute to COVID-19 seasonality, and that the relative importance of these drivers requires further investigation. A major challenge to predicting seasonality of infectious diseases are rapid, human-induced changes in the hitherto predictable seasonality of our planet, whose influence we review in a final outlook section. We conclude that a proactive multidisciplinary approach is warranted to predict, mitigate, and prevent seasonal infectious diseases in our complex, changing human-earth system.

scholarly journals Classification of unlabeled online media

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sakthi Kumar Arul Prakash ◽  
Conrad Tucker
Keyword(s):  
Social Media ◽  
Real World ◽  
Graphical Model ◽  
Ground Truth ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Social Media Networks ◽  
Online Social Media ◽  
Wide Range

AbstractThis work investigates the ability to classify misinformation in online social media networks in a manner that avoids the need for ground truth labels. Rather than approach the classification problem as a task for humans or machine learning algorithms, this work leverages user–user and user–media (i.e.,media likes) interactions to infer the type of information (fake vs. authentic) being spread, without needing to know the actual details of the information itself. To study the inception and evolution of user–user and user–media interactions over time, we create an experimental platform that mimics the functionality of real-world social media networks. We develop a graphical model that considers the evolution of this network topology to model the uncertainty (entropy) propagation when fake and authentic media disseminates across the network. The creation of a real-world social media network enables a wide range of hypotheses to be tested pertaining to users, their interactions with other users, and with media content. The discovery that the entropy of user–user and user–media interactions approximate fake and authentic media likes, enables us to classify fake media in an unsupervised learning manner.

scholarly journals Validation of an HPLC–MS/MS Method for the Determination of Plasma Ticagrelor and Its Active Metabolite Useful for Research and Clinical Practice

Molecules ◽  
2021 ◽  
Vol 26 (2) ◽  
pp. 278
Author(s):  
Jennifer Lagoutte-Renosi ◽  
Bernard Royer ◽  
Vahideh Rabani ◽  
Siamak Davani
Keyword(s):  
Active Metabolite ◽  
Plasma Concentrations ◽  
Antiplatelet Agent ◽  
High Plasma ◽  
Therapeutic Drug ◽  
Routine Practice ◽  
Daily Routine ◽  
Limit Of Quantification ◽  
Wide Range

Ticagrelor is an antiplatelet agent which is extensively metabolized in an active metabolite: AR-C124910XX. Ticagrelor antagonizes P2Y12 receptors, but recently, this effect on the central nervous system has been linked to the development of dyspnea. Ticagrelor-related dyspnea has been linked to persistently high plasma concentrations of ticagrelor. Therefore, there is a need to develop a simple, rapid, and sensitive method for simultaneous determination of ticagrelor and its active metabolite in human plasma to further investigate the link between concentrations of ticagrelor, its active metabolite, and side effects in routine practice. We present here a new method of quantifying both molecules, suitable for routine practice, validated according to the latest Food and Drug Administration (FDA) guidelines, with a good accuracy and precision (<15% respectively), except for the lower limit of quantification (<20%). We further describe its successful application to plasma samples for a population pharmacokinetics study. The simplicity and rapidity, the wide range of the calibration curve (2–5000 µg/L for ticagrelor and its metabolite), and high throughput make a broad spectrum of applications possible for our method, which can easily be implemented for research, or in daily routine practice such as therapeutic drug monitoring to prevent overdosage and occurrence of adverse events in patients.

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