scholarly journals Anatomy of a Protest: Spatial Information, Social Media, and Urban Space

2020 ◽  
Vol 6 (1) ◽  
pp. 205630511989732
Author(s):  
Alireza Karduni ◽  
Eric Sauda
Keyword(s):  
Social Media ◽  
Public Space ◽  
Urban Space ◽  
Language Processing ◽  
Local Community ◽  
Spatial Information ◽  
Social Media Data ◽  
Public Events ◽  
Use Of Social Media ◽  
Media Data

Black Lives Matter, like many modern movements in the age of information, makes significant use of social media as well as public space to demand justice. In this article, we study the protests in response to the shooting of Keith Lamont Scott by police in Charlotte, North Carolina, on September 2016. Our goal is to measure the significance of urban space within the virtual and physical network of protesters. Using a mixed-methods approach, we identify and study urban space and social media generated by these protests. We conducted interviews with protesters who were among the first to join the Keith Lamont Scott shooting demonstrations. From the interviews, we identify places that were significant in our interviewees’ narratives. Using a combination of natural language processing and social network analysis, we analyze social media data related to the Charlotte protests retrieved from Twitter. We found that social media, local community, and public space work together to organize and motivate protests and that public events such as protests cause a discernible increase in social media activity. Finally, we find that there are two distinct communities who engage social media in different ways; one group involved with social media, local community and urban space, and a second group connected almost exclusively through social media.

scholarly journals Perceiving Residents’ Festival Activities Based on Social Media Data: A Case Study in Beijing, China

2021 ◽  
Vol 10 (7) ◽  
pp. 474
Author(s):  
Bingqing Wang ◽  
Bin Meng ◽  
Juan Wang ◽  
Siyu Chen ◽  
Jian Liu
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Topic Model ◽  
Central Area ◽  
Classification Model ◽  
Social Media Data ◽  
Ring Road ◽  
Different Types ◽  
Spatial Differences ◽  
Media Data

Social media data contains real-time expressed information, including text and geographical location. As a new data source for crowd behavior research in the era of big data, it can reflect some aspects of the behavior of residents. In this study, a text classification model based on the BERT and Transformers framework was constructed, which was used to classify and extract more than 210,000 residents’ festival activities based on the 1.13 million Sina Weibo (Chinese “Twitter”) data collected from Beijing in 2019 data. On this basis, word frequency statistics, part-of-speech analysis, topic model, sentiment analysis and other methods were used to perceive different types of festival activities and quantitatively analyze the spatial differences of different types of festivals. The results show that traditional culture significantly influences residents’ festivals, reflecting residents’ motivation to participate in festivals and how residents participate in festivals and express their emotions. There are apparent spatial differences among residents in participating in festival activities. The main festival activities are distributed in the central area within the Fifth Ring Road in Beijing. In contrast, expressing feelings during the festival is mainly distributed outside the Fifth Ring Road in Beijing. The research integrates natural language processing technology, topic model analysis, spatial statistical analysis, and other technologies. It can also broaden the application field of social media data, especially text data, which provides a new research paradigm for studying residents’ festival activities and adds residents’ perception of the festival. The research results provide a basis for the design and management of the Chinese festival system.

scholarly journals A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness (Preprint)

2021 ◽  
Author(s):  
Vishal Dey ◽  
Peter Krasniak ◽  
Minh Nguyen ◽  
Clara Lee ◽  
Xia Ning
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Natural Language Processing ◽  
Data Analysis ◽  
Natural Language ◽  
Language Processing ◽  
Breast Implant ◽  
Public Attention ◽  
Social Media Data ◽  
Media Data

BACKGROUND A new illness can come to public attention through social media before it is medically defined, formally documented, or systematically studied. One example is a condition known as breast implant illness (BII), which has been extensively discussed on social media, although it is vaguely defined in the medical literature. OBJECTIVE The objective of this study is to construct a data analysis pipeline to understand emerging illnesses using social media data and to apply the pipeline to understand the key attributes of BII. METHODS We constructed a pipeline of social media data analysis using natural language processing and topic modeling. Mentions related to signs, symptoms, diseases, disorders, and medical procedures were extracted from social media data using the clinical Text Analysis and Knowledge Extraction System. We mapped the mentions to standard medical concepts and then summarized these mapped concepts as topics using latent Dirichlet allocation. Finally, we applied this pipeline to understand BII from several BII-dedicated social media sites. RESULTS Our pipeline identified topics related to toxicity, cancer, and mental health issues that were highly associated with BII. Our pipeline also showed that cancers, autoimmune disorders, and mental health problems were emerging concerns associated with breast implants, based on social media discussions. Furthermore, the pipeline identified mentions such as rupture, infection, pain, and fatigue as common self-reported issues among the public, as well as concerns about toxicity from silicone implants. CONCLUSIONS Our study could inspire future studies on the suggested symptoms and factors of BII. Our study provides the first analysis and derived knowledge of BII from social media using natural language processing techniques and demonstrates the potential of using social media information to better understand similar emerging illnesses. CLINICALTRIAL

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.

Employing the Sentiment Analysis Tool in NVivo 11 Plus on Social Media Data

2017 ◽  
pp. 175-244
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Analysis Tool ◽  
Attitudes And Beliefs ◽  
Social Media Data ◽  
Public Issues ◽  
Public Events ◽  
Brand Reputation ◽  
Text Corpora ◽  
Media Data

Sentiment analysis has been used to assess people's feelings, attitudes, and beliefs, ranging from positive to negative, on a variety of phenomena. Several new autocoding features in NVivo 11 Plus enable the capturing of sentiment analysis and extraction of themes from text datasets. This chapter describes eight scenarios in which these tools may be applied to social media data, to (1) profile egos and entities, (2) analyze groups, (3) explore metadata for latent public conceptualizations, (4) examine trending public issues, (5) delve into public concepts, (6) observe public events, (7) analyze brand reputation, and (8) inspect text corpora for emergent insights.

scholarly journals Mining social media for prescription medication abuse monitoring: a review and proposal for a data-centric framework

2019 ◽  
Vol 27 (2) ◽  
pp. 315-329 ◽  
Author(s):  
Abeed Sarker ◽  
Annika DeRoos ◽  
Jeanmarie Perrone
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Prescription Medication ◽  
Inclusion Criteria ◽  
Major Health Problem ◽  
Social Media Data ◽  
Use Of Data ◽  
Media Source ◽  
Multiple Characteristics ◽  
Media Data

Abstract Objective Prescription medication (PM) misuse and abuse is a major health problem globally, and a number of recent studies have focused on exploring social media as a resource for monitoring nonmedical PM use. Our objectives are to present a methodological review of social media–based PM abuse or misuse monitoring studies, and to propose a potential generalizable, data-centric processing pipeline for the curation of data from this resource. Materials and Methods We identified studies involving social media, PMs, and misuse or abuse (inclusion criteria) from Medline, Embase, Scopus, Web of Science, and Google Scholar. We categorized studies based on multiple characteristics including but not limited to data size; social media source(s); medications studied; and primary objectives, methods, and findings. Results A total of 39 studies met our inclusion criteria, with 31 (∼79.5%) published since 2015. Twitter has been the most popular resource, with Reddit and Instagram gaining popularity recently. Early studies focused mostly on manual, qualitative analyses, with a growing trend toward the use of data-centric methods involving natural language processing and machine learning. Discussion There is a paucity of standardized, data-centric frameworks for curating social media data for task-specific analyses and near real-time surveillance of nonmedical PM use. Many existing studies do not quantify human agreements for manual annotation tasks or take into account the presence of noise in data. Conclusion The development of reproducible and standardized data-centric frameworks that build on the current state-of-the-art methods in data and text mining may enable effective utilization of social media data for understanding and monitoring nonmedical PM use.

scholarly journals Inferring Activities from Social Media Data

2017 ◽  
Vol 2666 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Emmanouil Chaniotakis ◽  
Constantinos Antoniou ◽  
Georgia Aifadopoulou ◽  
Loukas Dimitriou
Keyword(s):  
Social Media ◽  
Classification Performance ◽  
Classification Algorithms ◽  
Social Media Data ◽  
Use Of Social Media ◽  
Definition Of ◽  
The Individual ◽  
Transportation Research ◽  
Direct Use ◽  
Media Data

Social media produce an unprecedented amount of information that can be extracted and used in transportation research, with one of the most promising areas being the inference of individuals’ activities. Whereas most studies in the literature focus on the direct use of social media data, this study presents an efficient framework that follows a user-centric approach for the inference of users’ activities from social media data. The framework was applied to data from Twitter, combined with inferred data from Foursquare that contains information about the type of location visited. The users’ data were then classified with a density-based spatial classification algorithm that allows for the definition of commonly visited locations, and the individual-based data were augmented with the known activity definition from Foursquare. On the basis of the known activities and the Twitter text, a set of classification algorithms was applied for the inference of activities. The results are discussed according to the types of activities recognized and the classification performance. The classification results allow for a wide application of the framework in the exploration of the activity space of individuals.

scholarly journals Journalists’ Use of Social Media to Infer Public Opinion: The citizens’ perspective

2021 ◽  
Author(s):  
Elizabeth Dubois ◽  
Anatoliy Gruzd ◽  
Jenna Jacobson
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Online Survey ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Social Media Platforms ◽  
Use Of Social Media ◽  
Traditional Approaches ◽  
Journalistic Practice ◽  
Media Data

Journalists increasingly use social media data to infer and report public opinion by quoting social media posts, identifying trending topics, and reporting general sentiment. In contrast to traditional approaches of inferring public opinion, citizens are often unaware of how their publicly available social media data is being used and how public opinion is constructed using social media analytics. In this exploratory study based on a census-weighted online survey of Canadian adults (N=1,500), we examine citizens’ perceptions of journalistic use of social media data. We demonstrate that: (1) people find it more appropriate for journalists to use aggregate social media data rather than personally identifiable data; (2) people who use more social media are more likely to positively perceive journalistic use of social media data to infer public opinion; and (3) the frequency of political posting is positively related to acceptance of this emerging journalistic practice, which suggests some citizens want to be heard publicly on social media while others do not. We provide recommendations for journalists on the ethical use of social media data and social media platforms on opt-in functionality.

Self-Comparison in the Digital World

2020 ◽  
pp. 231-240
Author(s):  
Clara Moningka
Keyword(s):  
Social Media ◽  
Emerging Adults ◽  
Self Esteem ◽  
Psychological Effects ◽  
Internet Service ◽  
Social Media Data ◽  
Digital World ◽  
The World ◽  
Use Of Social Media ◽  
Media Data

In this chapter, the author is interested in studying self-comparison in social media and its effect to the self-esteem in emerging adults. In Indonesia, social media are widely used by various groups. Jakarta is even referred as the capital of a text-based social media. Data in 2016 indicated that social media users in Indonesia have reached high ranking. Indonesia ranked fourth in the world for social media users and ranked first with Facebook with 111 million users, followed by Twitter. Indonesian Internet Service Provider Association explained that the biggest users were dominated by adolescents, amounting to 75.50% of the total users. The use of social media can be influenced by collective culture. This culture can influence how individuals evaluate themselves, including their self-esteem. The topic of the psychological effects of social media has been much discussed. A lot of research conducted on the effect of social on development of self-esteem. Social media becoming a place for comparing oneself to others and it turn out it has a great effect.

Social media usage in business decision-making: the case of Malaysian small hospitality organisations

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
A.K. Siti-Nabiha ◽  
Norfarah Nordin ◽  
Boon Kar Poh
Keyword(s):  
Social Media ◽  
Data Gathering ◽  
Customer Relationship ◽  
Business Decisions ◽  
Business Decision ◽  
Social Media Data ◽  
Content Type ◽  
Analysis Process ◽  
Use Of Social Media ◽  
Media Data

PurposeThe purpose of this paper was to examine how small- and medium-sized hospitality organisations engage with social media and how social media data are used by their managers to inform business decisions.Design/methodology/approachA qualitative approach was used in this research in which interviews were conducted with top management, comprising the owners/directors and other key managers from small- and medium-sized organisations based in Penang, Malaysia. Fan and Gordon's (2014) categorisation of the social media data analysis process and Simon's (1995) concept of the interactive and diagnostic usage of data were used in the analysis of data.FindingsThe managers of small- and medium-sized hospitality organisations engage with social media for customer relationship management and the understanding of key main competitors. Social media is used to understand, build and manage relationships with current and potential customers; these activities are also linked to actions taken to protect a company's reputation. Even though, for the companies concerned, data gathering is still at the capture stage with no formal procedures and processes in place, the data are utilised in an interactive way to inform two areas’ major business decisions-making, i.e. those related to pricing and promotion and the strategic formulation and reorientation of the business.Research limitations/implicationsThe respondents of this study were mainly from smaller hospitality organisations. Hence, the insights gained are limited to the context of smaller hotels.Originality/valueA significant number of social media studies within the hospitality sector have focussed on marketing aspects. This study explored the wider use of social media in the case of smaller hospitality organisations and how they compete and position themselves in the competitive hospitality industry.

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