scholarly journals USING CROWDSOURCED DATA (TWITTER & FACEBOOK) TO DELINEATE THE ORIGIN AND DESTINATION OF COMMUTERS OF THE GAUTRAIN PUBLIC TRANSIT SYSTEM IN SOUTH AFRICA

2016 ◽  
Vol III-2 ◽  
pp. 143-150 ◽  
Author(s):  
T. Moyo ◽  
W. Musakwa
Keyword(s):  
Social Media ◽  
Prediction Models ◽  
Transit System ◽  
Social Media Data ◽  
Points Of Interest ◽  
Crowdsourced Data ◽  
Data Source ◽  
Media Data ◽  
Inform Decision Making ◽  
Concentration Levels

The study of commuters’ origins and destinations (O_D) promises to assist transportation planners with prediction models to inform decision making. Conventionally O_D surveys are undertaken through travel surveys and traffic counts, however data collection for these surveys has historically proven to be time consuming and having a strain on human resources, thus a need for an alternative data source arises. This study combines the use social media data and geographic information systems in the creation of a model for origin and destination surveys. The model tests the potential of using big data from Echo echo software which contains Twitter and Facebook data obtained from social media users in Gauteng. This data contains geo-location and it is used to determine origin and destination as well as concentration levels of Gautrain commuters. A kriging analysis was performed on the data to determine the O-D and concentration levels of Gautrain users. The results reveal the concentration of Gautrain commuters at various points of interest that is where they work, live or socialise. The results from the study highlight which nodes attract the most commuters and also possible locations for the expansion for Gautrain. Lastly, the study also highlights some weakness of crowdsourced data for informing transportation planning.

scholarly journals Using social media data to map the areas most affected by ISIS in Syria

2020 ◽  
Vol 26 (1) ◽  
pp. 464-470
Author(s):  
Mohamad Hasan
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Geographical Information ◽  
Data Mapping ◽  
Islamic State ◽  
Social Media Data ◽  
The Social ◽  
Mapping Process ◽  
Data Source ◽  
Media Data

This paper presents a model to collect, save, geocode, and analyze social media data. The model is used to collect and process the social media data concerned with the ISIS terrorist group (the Islamic State in Iraq and Syria), and to map the areas in Syria most affected by ISIS accordingly to the social media data. Mapping process is assumed automated compilation of a density map for the geocoded tweets. Data mined from social media (e.g., Twitter and Facebook) is recognized as dynamic and easily accessible resources that can be used as a data source in spatial analysis and geographical information system. Social media data can be represented as a topic data and geocoding data basing on the text of the mined from social media and processed using Natural Language Processing (NLP) methods. NLP is a subdomain of artificial intelligence concerned with the programming computers to analyze natural human language and texts. NLP allows identifying words used as an initial data by developed geocoding algorithm. In this study, identifying the needed words using NLP was done using two corpora. First corpus contained the names of populated places in Syria. The second corpus was composed in result of statistical analysis of the number of tweets and picking the words that have a location meaning (i.e., schools, temples, etc.). After identifying the words, the algorithm used Google Maps geocoding API in order to obtain the coordinates for posts.

scholarly journals EXTRACTING AND COMPARING PLACES USING GEO-SOCIAL MEDIA

2015 ◽  
Vol II-3/W5 ◽  
pp. 311-316
Author(s):  
F. O. Ostermann ◽  
H. Huang ◽  
G. Andrienko ◽  
N. Andrienko ◽  
C. Capineri ◽  
...  
Keyword(s):  
Social Media ◽  
Semantic Similarity ◽  
Data Set ◽  
Social Media Data ◽  
Temporal Clustering ◽  
Depth Study ◽  
Data Source ◽  
Spatio Temporal ◽  
Media Data

Increasing availability of Geo-Social Media (e.g. Facebook, Foursquare and Flickr) has led to the accumulation of large volumes of social media data. These data, especially geotagged ones, contain information about perception of and experiences in various environments. Harnessing these data can be used to provide a better understanding of the semantics of places. We are interested in the similarities or differences between different Geo-Social Media in the description of places. This extended abstract presents the results of a first step towards a more in-depth study of semantic similarity of places. Particularly, we took places extracted through spatio-temporal clustering from one data source (Twitter) and examined whether their structure is reflected semantically in another data set (Flickr). Based on that, we analyse how the semantic similarity between places varies over space and scale, and how Tobler's first law of geography holds with regards to scale and places.

scholarly journals Distant Supervision for Mental Health Management in Social Media: Suicide Risk Classification System Development Study

10.2196/26119 ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. e26119
Author(s):  
Guanghui Fu ◽  
Changwei Song ◽  
Jianqiang Li ◽  
Yue Ma ◽  
Pan Chen ◽  
...  
Keyword(s):  
Social Media ◽  
Suicide Risk ◽  
Health Management ◽  
System Development ◽  
Common People ◽  
Web Based ◽  
Social Media Data ◽  
Distant Supervision ◽  
Data Source ◽  
Media Data

Background Web-based social media provides common people with a platform to express their emotions conveniently and anonymously. There have been nearly 2 million messages in a particular Chinese social media data source, and several thousands more are generated each day. Therefore, it has become impossible to analyze these messages manually. However, these messages have been identified as an important data source for the prevention of suicide related to depression disorder. Objective We proposed in this paper a distant supervision approach to developing a system that can automatically identify textual comments that are indicative of a high suicide risk. Methods To avoid expensive manual data annotations, we used a knowledge graph method to produce approximate annotations for distant supervision, which provided a basis for a deep learning architecture that was built and refined by interactions with psychology experts. There were three annotation levels, as follows: free annotations (zero cost), easy annotations (by psychology students), and hard annotations (by psychology experts). Results Our system was evaluated accordingly and showed that its performance at each level was promising. By combining our system with several important psychology features from user blogs, we obtained a precision of 80.75%, a recall of 75.41%, and an F1 score of 77.98% for the hardest test data. Conclusions In this paper, we proposed a distant supervision approach to develop an automatic system that can classify high and low suicide risk based on social media comments. The model can therefore provide volunteers with early warnings to prevent social media users from committing suicide.

Studies of depression and anxiety using Reddit as a data source: Scoping review (Preprint)

2021 ◽  
Author(s):  
Nick Boettcher
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Scoping Review ◽  
Primary Data ◽  
Technical Solution ◽  
Depression And Anxiety ◽  
Social Media Data ◽  
Primary Data Source ◽  
Data Source ◽  
Media Data

BACKGROUND The study of depression and anxiety using publicly available social media data is a research activity that has grown considerably over the last decade. The discussion platform Reddit has become a popular social media data source in this nascent area of study, in part because of the unique ways in which the platform is facilitative of research. To date, no work has been done to synthesize existing studies of depression and anxiety using Reddit. OBJECTIVE The objective of this review is to understand the scope and nature of research using Reddit as a primary data source for studying depression and anxiety. METHODS A scoping review was conducted using the Arksey and O’Malley framework. Academic databases searched include MEDLINE/PubMed, EMBASE, CINAHL, PsycINFO, PsycARTICLES, Scopus, ScienceDirect, IEEE Xplore, and ACM database. Inclusion criteria were developed using the Participants/Concept/Context framework outlined by the Joanna Briggs Institute Scoping Review Methodology Group. Eligible studies featured a methodological focus on analyzing depression and/or anxiety using naturalistic written expressions from Reddit users as the primary data source. RESULTS 54 Studies were included for review. Tables and corresponding analysis delineate key methodological features including a comparatively larger focus on depression versus anxiety, an even split of original and premade datasets, a favored analytic focus on classifying the mental health states of Reddit users, and practical implications often recommending new methods of professionally-driven mental health monitoring and outreach for Reddit users. CONCLUSIONS Studies of depression and anxiety using Reddit data are currently driven by a prevailing methodology which favors a technical, solution-based orientation. Researchers interested in advancing this research area will benefit from further consideration of conceptual issues surrounding interpretation of Reddit data with the medical model of mental health. Further efforts are also needed to locate accountability and autonomy within practice implications suggesting new forms of engagement with Reddit users.

scholarly journals A Comparative Study of Spatial and Temporal Preferences for Waterfronts in Wuhan based on Gender Differences in Check-In Behavior

2019 ◽  
Vol 8 (9) ◽  
pp. 413 ◽  
Author(s):  
Wu ◽  
Li ◽  
Ma
Keyword(s):  
Gender Differences ◽  
Social Media ◽  
Geographical Location ◽  
Distribution Characteristics ◽  
Social Media Data ◽  
Men And Women ◽  
Male And Female ◽  
Points Of Interest ◽  
And Gender ◽  
Media Data

The geographical location and check-in frequency of social platform users indicate their personal preferences and intentions for space. On the basis of social media data and gender differences, this study analyzes Weibo users’ preferences and the reasons behind these preferences for the waterfronts of the 21 major lakes within Wuhan’s Third Ring Road, in accordance with users’ check-in behaviors. According to the distribution characteristics of the waterfronts’ points of interest, this study explores the preferences of male and female users for waterfronts and reveals, through the check-in behaviors of Weibo users, the gender differences in the preference and willingness of these users to choose urban waterfronts. Results show that men and women check in significantly more frequently on weekends than on weekdays. Women are more likely than men to check in at waterfronts. Significant differences in time and space exist between male and female users’ preferences for different lakes.

scholarly journals REALISASI TINDAK KESANTUNAN POSITIF DALAM WACANA AKADEMIK DI MEDIA SOSIAL BERPERSPEKTIF HUMANIS/POSITIVE POLITENESS OF ACADEMIC DISCOURSE TAKING PLACE IN SOCIAL MEDIA INTERACTION: A HUMANITY PERSPECTIVE

Aksara ◽  
2021 ◽  
Vol 32 (2) ◽  
pp. 323-338
Author(s):  
Hari Kusmanto ◽  
Nadia Puji Ayu ◽  
Harun Joko Prayitno ◽  
Laili Etika Rahmawati ◽  
Dini Restiyanti Pratiwi ◽  
...  
Keyword(s):  
Social Media ◽  
Academic Discourse ◽  
Human Values ◽  
Social Media Data ◽  
Positive Communication ◽  
Media Interaction ◽  
Data Source ◽  
High Attitude ◽  
Equivalent Method ◽  
Media Data

Abstrak Studi ini bertujuan mendeskripsikan wujud kesantunan berkomunikasi dalam media sosial WhatsApp antara mahasiswa dan dosen. Studi ini adalah kualitatif. Data dalam studi ini adalah kalimat-kalimat santun dalam wacana akademik di media sosial. Sumber data dalam studi ini adalah tuturan wacana akademik di media sosial. Pengumpulan data dalam studi ini menggunakan metode dokumentasi, simak, dan dilanjutkan dengan teknik catat. Analisis data dalam studi ini dilakukan dengan metode padan intralingual; padan pragmatis dan diperkuat dengan teknik analisis kesantunan Brown dan Levinson berperspektif humanis. Hasil studi ini menunjukkan tindak kesantunan positif meliputi: (1) mengucapkan terima kasih sebagai penghormatan kepada mitra tutur, 48%; (2) memberikan pertanyaan sebagai wujud perhatian kepada mitra tutur, 8%; (3) memberikan informasi kepada mitra tutur sebagai wujud kepedulian, 18%; (4) menunjukkan keoptimisan kepada mitra tutur supaya termotivasi, 4%; (5) memberikan hadiah kepada mitra tutur dengan memberikan dukungan, 4%; (6) mengucapkan salam kepada mitra tutur sebagai upaya mendoakan kebaikan kepada mitra tutur, 8%; dan (7) menggunakan penanda identitas sebagai wujud menjalin solidaritas antara penutur dan mitra tutur, 10%. Hal ini menunjukkan mahasiswa memiliki sikap penghormatan yang tinggi kepada dosen dengan menunjukkan komunikasi bernada positif. Tindak kesantunan mengucapkan terima kasih, memberikan informasi yang dibutuhkan mitra tutur, menunjukkan sikap percaya diri, mengucapkan salam merupakan wujud komunikasi yang berperspektif humanis, yakni menjunjung nilai-nilai kemanusian. Penelitian ini bermanfaat dalam membangun komunikasi pembelajaran yang berorientasi pada kesantunan berbahasa yang memartabatkan nilai-nilai humanitas dalam pembelajaran. Kata kunci: kesantunan positif, akademik, media sosial, humanis Abstract This study aims to describe the form of politeness in communicating on WhatsApp social media between students and lecturers. This study is qualitative. The data in this study are polite sentences in academic discourse on social media. The data source in this study is the speech of academic discourse on social media. Data collection in this study uses the documentation method, refer to it, and proceed with note taking technique. Data analysis in this study was carried out using the intralingual equivalent method; pragmatic equivalent and strengthened by Brown and Levinson’s politeness analysis techniques with a sweet perspective. The results of this study show positive politeness actions include: (1) Thank you for the speech partner observer 48%; (2) giving questions as a form of attention to the speech partners 8%; (3) providing information to the speech partners as a form of concern 18%; (4) showing optimism for the speech partners to be motivated 4%; (5) giving gifts to speech partners by giving support 4%; (6) greeting the speech partners in an effort to pray for the kindness of the speech partners 8%; and (7) using identity markers as a form of establishing solidarity between the speaker and the speech partner 10%.. ISSN 0854-3283 (Print), ISSN 2580-0353 (Online) , Vol. 32, No. 2, Desember 2020 323 Realisasi Tindak Kesantunan Positif dalam Wacana Akademik di Media Sosial Berperspektif Humanitas Halaman 323 — 338 (Hari Kusmanto, Nadia P. Ayu, Harun J. Prayitno, Laili E. Rahmawati, Dini R. Pratiwi, dan Tri Santoso) This shows students have a high attitude of respect for lecturers by showing positive communication. Actions of thanksgiving, giving information needed by the speech partner, showing self-con dence, greeting is a form of communication with a humanist perspective, namely upholding human values. This research is useful in building learning communication that is oriented towards language politeness that digni es human values in learning. Keywords: positive politeness, academic, social media, humanity 

scholarly journals Social media as a big public health data source: review of the international bibliography

2015 ◽  
Author(s):  
Evika Karamagioli
Keyword(s):  
Public Health ◽  
Social Media ◽  
Big Data ◽  
Data Extraction ◽  
Online Communication ◽  
World Health ◽  
Health Crisis ◽  
Social Media Data ◽  
Data Source ◽  
Media Data

Background: As the use of social media creates huge amounts of data, the need for big data analysis has to synthesize the information and determine which actions is generated. Online communication channels such as Facebook, Twitter, Instagram etc provide a wealth of passively collected data that may be mined for public health purposes such as health surveillance, health crisis management, and last but not least health promotion and education. Objective: We explore international bibliography on the potential role and perceptive of use for social media as a big data source for public health purposes. Method: Systematic literature review. Data extraction and synthesis was performed with the use of thematic analysis. Results: Examples of those currently collecting and analyzing big data from generated social content include scientists who are working with the Centers for Disease Control and Prevention to track the spread of flu by analyzing what user searches, and the World Health Organization is working on disaster management relief. But what exactly do we do with this big social media data? We can track real-time trends and understand them quicker through the platforms and processing services. By processing this big social media data, it is possible to determine specific patterns in conversation topics, users behaviors, overall trends and influencers, sociodemographic characteristics, lifestyle behaviors, and social and cultural constructs. Conclusion: The key to fostering big data and social media converge is process and analyze the right data that may be mined for purposes of public health, so as to provide strategic insights for planning, execution and measurement of effective and efficient public health interventions. In this effort, political, economic and legal obstacles need to be seriously considered.

scholarly journals Identification Of Multifunctional Urban Activity Centers In Tokyo

2021 ◽  
Vol 14 (2) ◽  
pp. 83-91
Author(s):  
Vadim I. Boratinskii ◽  
Irina S. Tikhotskaya
Keyword(s):  
Social Media ◽  
Complex Method ◽  
Urban Structure ◽  
Distribution Analysis ◽  
Data Types ◽  
Spatial Autoregression ◽  
Social Media Data ◽  
Points Of Interest ◽  
Media Data ◽  
Urban Activity

Identification of urban activity centers is among the most important components of the urban structure study, it is necessary for reasonable planning, regulation of traffic flows and other practical measures. The purpose of this paper is to design a complex method to identify urban activity centers based on different but universal data types. In this study, we used social media data (Twitter) since it guarantees regular updates and does not rely on administrative borders and points of interest database that was considered a 'hard' representation of multifunctional urban activities. A large amount of geotagged tweets was processed by means of statistical modelling (spatial autoregression) and combined with the distribution analysis of points of interest. This allowed to identify the local centers of urban activity within 23 special wards of Tokyo more objectively and precisely than when only based on the social media data. Thereafter, delimitated centers were classified in order to define and describe their main functional and spatial characteristics. As a result of the study, railway transport was identified as the main attraction factor of the urban activity; the modern urban structure of Tokyo was identified and mapped; a new comprehensive method for identification of urban activity centers was developed and five classes of urban activity centers were defined and described.

Distant Supervision for Mental Health Management in Social Media: Suicide Risk Classification System Development Study (Preprint)

2020 ◽  
Author(s):  
Guanghui Fu ◽  
Changwei Song ◽  
Jianqiang Li ◽  
Yue Ma ◽  
Pan Chen ◽  
...  
Keyword(s):  
Social Media ◽  
Suicide Risk ◽  
Health Management ◽  
System Development ◽  
Common People ◽  
Web Based ◽  
Social Media Data ◽  
Distant Supervision ◽  
Data Source ◽  
Media Data

BACKGROUND Web-based social media provides common people with a platform to express their emotions conveniently and anonymously. There have been nearly 2 million messages in a particular Chinese social media data source, and several thousands more are generated each day. Therefore, it has become impossible to analyze these messages manually. However, these messages have been identified as an important data source for the prevention of suicide related to depression disorder. OBJECTIVE We proposed in this paper a distant supervision approach to developing a system that can automatically identify textual comments that are indicative of a high suicide risk. METHODS To avoid expensive manual data annotations, we used a knowledge graph method to produce approximate annotations for distant supervision, which provided a basis for a deep learning architecture that was built and refined by interactions with psychology experts. There were three annotation levels, as follows: free annotations (zero cost), easy annotations (by psychology students), and hard annotations (by psychology experts). RESULTS Our system was evaluated accordingly and showed that its performance at each level was promising. By combining our system with several important psychology features from user blogs, we obtained a precision of 80.75%, a recall of 75.41%, and an F1 score of 77.98% for the hardest test data. CONCLUSIONS In this paper, we proposed a distant supervision approach to develop an automatic system that can classify high and low suicide risk based on social media comments. The model can therefore provide volunteers with early warnings to prevent social media users from committing suicide.

Health Issues associated with the Use of Social Media by the Public and Researchers amid COVID-19: Insights and Practical Solutions (Preprint)

2020 ◽  
Author(s):  
Zhaohui Su
Keyword(s):  
Social Media ◽  
Underserved Populations ◽  
Social Needs ◽  
Health Issues ◽  
Social Media Data ◽  
The Public ◽  
Vulnerable Communities ◽  
Data Source ◽  
Use Of Social Media ◽  
Media Data

UNSTRUCTURED Social media use has grown exponentially amid COVID-19: when physical interactions become dangerous and unlikely, people turn to social media to meet their communication and social needs. However, though social media offer many benefits to users, prolonged use of social media may expose individuals to pronounced risks of facing twin crises simultaneously: the COVID-19 pandemic and the COVID-19 infodemic. While researchers set out to address these issues, along with other COVID-19 phenomena, they may need to be vigilant with using social media data as the main data source: due to a lack of representation of underserved populations (e.g., older adults), research based on social media data may offer limited insights into how to mitigate health disparities experienced by the society’s most vulnerable communities. To address these issues, this paper first discusses the crux of these problems and subsequently, offers practical solutions that have the potential to effectively mitigate these issues.

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