scholarly journals #METOO AND INTERSECTIONALISM: "RADICAL COMMUNITY HEALING" OR "VOYEURISTIC TRAUMA PORN?"

2019 ◽  
Vol 2019 ◽  
Author(s):  
Verity Trott
Keyword(s):  
Sexual Abuse ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
White Women ◽  
Large Data ◽  
Data Set ◽  
Mainstream Media ◽  
And Gender ◽  
The Many

In October 2017, millions of people reflected on their experiences of sexual abuse and harassment, publicly sharing their testimonials in an expression of global vulnerability using the hashtag #MeToo. Many of the tweets portrayed the angst and distress individuals experienced in their decision to participate, indicating the psychological costs of engaging with #MeToo. Further, some tweets expressed frustration at the re-appropriated nature of the campaign and the collective feeling of an “intersectional betrayal” by white women and feminists who dominated the mainstream media reporting of the movement. This research foregrounds the intersectional concerns that result from the scale and reach of the millions of testimonials suspended online that constitute the #MeToo movement. It highlights how the many stories that have circulated the online sphere obscure the absence and recognition of marginalised women and those who are already more vulnerable in regards to experiencing sexual assault. The paper adopts an intersectional framework, as conceptualised by Crenshaw (1991), to further an understanding of how race, class, and gender collide and how subordination can be reproduced within feminist protests. Drawing on a large data set of tweets, this research combines content, discourse and social network analysis to examine the narratives related to participation. The paper highlights the experiences and reflections of users who self-identified as queer, disabled, or a person of colour within their tweets. A social network analysis is also used to visualise a snapshot of the affective publics that arose at the beginning and to illustrate how systems of oppression converge.

Who is the boss? Identifying key roles in telecom fraud network via centrality-guided deep random walk

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi-Chun Chang ◽  
Kuan-Ting Lai ◽  
Seng-Cho T. Chou ◽  
Wei-Chuan Chiang ◽  
Yuan-Chen Lin
Keyword(s):  
Social Network ◽  
Random Walk ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Random Walks ◽  
Interpersonal Relationships ◽  
Interaction Network ◽  
Data Set ◽  
Content Type ◽  
Structural Equivalence

PurposeTelecommunication (telecom) fraud is one of the most common crimes and causes the greatest financial losses. To effectively eradicate fraud groups, the key fraudsters must be identified and captured. One strategy is to analyze the fraud interaction network using social network analysis. However, the underlying structures of fraud networks are different from those of common social networks, which makes traditional indicators such as centrality not directly applicable. Recently, a new line of research called deep random walk has emerged. These methods utilize random walks to explore local information and then apply deep learning algorithms to learn the representative feature vectors. Although effective for many types of networks, random walk is used for discovering local structural equivalence and does not consider the global properties of nodes.Design/methodology/approachThe authors proposed a new method to combine the merits of deep random walk and social network analysis, which is called centrality-guided deep random walk. By using the centrality of nodes as edge weights, the authors’ biased random walks implicitly consider the global importance of nodes and can thus find key fraudster roles more accurately. To evaluate the authors’ algorithm, a real telecom fraud data set with around 562 fraudsters was built, which is the largest telecom fraud network to date.FindingsThe authors’ proposed method achieved better results than traditional centrality indices and various deep random walk algorithms and successfully identified key roles in a fraud network.Research limitations/implicationsThe study used co-offending and flight record to construct a criminal network, more interpersonal relationships of fraudsters, such as friendships and relatives, can be included in the future.Originality/valueThis paper proposed a novel algorithm, centrality-guided deep random walk, and applied it to a new telecom fraud data set. Experimental results show that the authors’ method can successfully identify the key roles in a fraud group and outperform other baseline methods. To the best of the authors’ knowledge, it is the largest analysis of telecom fraud network to date.

scholarly journals COVID-19 and the “Film Your Hospital” Conspiracy Theory: Social Network Analysis of Twitter Data (Preprint)

2020 ◽  
Author(s):  
Wasim Ahmed ◽  
Francesc López Seguí ◽  
Josep Vidal-Alaball ◽  
Matthew S Katz
Keyword(s):  
Public Health ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information Source ◽  
Conspiracy Theory ◽  
Sources Of Information ◽  
Data Set ◽  
Conspiracy Theories ◽  
Twitter Data

BACKGROUND During the COVID-19 pandemic, a number of conspiracy theories have emerged. A popular theory posits that the pandemic is a hoax and suggests that certain hospitals are “empty.” Research has shown that accepting conspiracy theories increases the likelihood that an individual may ignore government advice about social distancing and other public health interventions. Due to the possibility of a second wave and future pandemics, it is important to gain an understanding of the drivers of misinformation and strategies to mitigate it. OBJECTIVE This study set out to evaluate the #FilmYourHospital conspiracy theory on Twitter, attempting to understand the drivers behind it. More specifically, the objectives were to determine which online sources of information were used as evidence to support the theory, the ratio of automated to organic accounts in the network, and what lessons can be learned to mitigate the spread of such a conspiracy theory in the future. METHODS Twitter data related to the #FilmYourHospital hashtag were retrieved and analyzed using social network analysis across a 7-day period from April 13-20, 2020. The data set consisted of 22,785 tweets and 11,333 Twitter users. The Botometer tool was used to identify accounts with a higher probability of being bots. RESULTS The most important drivers of the conspiracy theory are ordinary citizens; one of the most influential accounts is a Brexit supporter. We found that YouTube was the information source most linked to by users. The most retweeted post belonged to a verified Twitter user, indicating that the user may have had more influence on the platform. There was a small number of automated accounts (bots) and deleted accounts within the network. CONCLUSIONS Hashtags using and sharing conspiracy theories can be targeted in an effort to delegitimize content containing misinformation. Social media organizations need to bolster their efforts to label or remove content that contains misinformation. Public health authorities could enlist the assistance of influencers in spreading antinarrative content.

scholarly journals COVID-19 and the “Film Your Hospital” Conspiracy Theory: Social Network Analysis of Twitter Data

10.2196/22374 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e22374 ◽  
Author(s):  
Wasim Ahmed ◽  
Francesc López Seguí ◽  
Josep Vidal-Alaball ◽  
Matthew S Katz
Keyword(s):  
Public Health ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information Source ◽  
Conspiracy Theory ◽  
Sources Of Information ◽  
Data Set ◽  
Conspiracy Theories ◽  
Twitter Data

Background During the COVID-19 pandemic, a number of conspiracy theories have emerged. A popular theory posits that the pandemic is a hoax and suggests that certain hospitals are “empty.” Research has shown that accepting conspiracy theories increases the likelihood that an individual may ignore government advice about social distancing and other public health interventions. Due to the possibility of a second wave and future pandemics, it is important to gain an understanding of the drivers of misinformation and strategies to mitigate it. Objective This study set out to evaluate the #FilmYourHospital conspiracy theory on Twitter, attempting to understand the drivers behind it. More specifically, the objectives were to determine which online sources of information were used as evidence to support the theory, the ratio of automated to organic accounts in the network, and what lessons can be learned to mitigate the spread of such a conspiracy theory in the future. Methods Twitter data related to the #FilmYourHospital hashtag were retrieved and analyzed using social network analysis across a 7-day period from April 13-20, 2020. The data set consisted of 22,785 tweets and 11,333 Twitter users. The Botometer tool was used to identify accounts with a higher probability of being bots. Results The most important drivers of the conspiracy theory are ordinary citizens; one of the most influential accounts is a Brexit supporter. We found that YouTube was the information source most linked to by users. The most retweeted post belonged to a verified Twitter user, indicating that the user may have had more influence on the platform. There was a small number of automated accounts (bots) and deleted accounts within the network. Conclusions Hashtags using and sharing conspiracy theories can be targeted in an effort to delegitimize content containing misinformation. Social media organizations need to bolster their efforts to label or remove content that contains misinformation. Public health authorities could enlist the assistance of influencers in spreading antinarrative content.

scholarly journals Identification of cohesive subgroups in a university hall of residence during the COVID-19 pandemic using a social network analysis approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pilar Marqués-Sánchez ◽  
Arrate Pinto-Carral ◽  
Tania Fernández-Villa ◽  
Ana Vázquez-Casares ◽  
Cristina Liébana-Presa ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
University Students ◽  
Global Network ◽  
Case Study Methodology ◽  
And Gender ◽  
The One ◽  
Relationship Structure ◽  
The University

AbstractThe aims: (i) analyze connectivity between subgroups of university students, (ii) assess which bridges of relational contacts are essential for connecting or disconnecting subgroups and (iii) to explore the similarities between the attributes of the subgroup nodes in relation to the pandemic context. During the COVID-19 pandemic, young university students have experienced significant changes in their relationships, especially in the halls of residence. Previous research has shown the importance of relationship structure in contagion processes. However, there is a lack of studies in the university setting, where students live closely together. The case study methodology was applied to carry out a descriptive study. The participation consisted of 43 university students living in the same hall of residence. Social network analysis has been applied for data analysis. Factions and Girvan–Newman algorithms have been applied to detect the existing cohesive subgroups. The UCINET tool was used for the calculation of the SNA measure. A visualization of the global network will be carried out using Gephi software. After applying the Girvan–Newman and Factions, in both cases it was found that the best division into subgroups was the one that divided the network into 4 subgroups. There is high degree of cohesion within the subgroups and a low cohesion between them. The relationship between subgroup membership and gender was significant. The degree of COVID-19 infection is related to the degree of clustering between the students. College students form subgroups in their residence. Social network analysis facilitates an understanding of structural behavior during the pandemic. The study provides evidence on the importance of gender, race and the building where they live in creating network structures that favor, or not, contagion during a pandemic.

The Rise of Fashion Informatics: A Case of Data-Mining-Based Social Network Analysis in Fashion

2018 ◽  
Vol 37 (2) ◽  
pp. 87-102 ◽  
Author(s):  
Li Zhao ◽  
Chao Min
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Large Scale ◽  
Dynamic Network ◽  
Fashion Industry ◽  
Cognitive Computing ◽  
Data Set

With the advent of modern cognitive computing technologies, fashion informatics researchers contribute to the academic and professional discussion about how a large-scale data set is able to reshape the fashion industry. Data-mining-based social network analysis is a promising area of fashion informatics to investigate relations and information flow among fashion units. By adopting this pragmatic approach, we provide dynamic network visualizations of the case of Paris Fashion Week. Three time periods were researched to monitor the formulation and mobilization of social media users’ discussions of the event. Initial textual data on social media were crawled, converted, calculated, and visualized by Python and Gephi. The most influential nodes (hashtags) that function as junctions and the distinct hashtag communities were identified and represented visually as graphs. The relations between the contextual clusters and the role of junctions in linking these clusters were investigated and interpreted.

Social network analysis in supply chain management research

2016 ◽  
Vol 46 (8) ◽  
pp. 740-762 ◽  
Author(s):  
Barbara K. Wichmann ◽  
Lutz Kaufmann
Keyword(s):  
Supply Chain ◽  
Social Network ◽  
Supply Chain Management ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Chain Management ◽  
Content Type ◽  
Network Research ◽  
The Many ◽  
The Right

Purpose The purpose of this paper is to investigate when and how to best use social network analysis (SNA) in the supply chain management (SCM) discipline. In doing so, the study identifies SCM phenomena that have been examined from a social network perspective (SNA approach) in the SCM literature and highlights additional SCM phenomena that would be worth investigating using social network research. Then, the study critically investigates the application of SNA as a methodology (SNA method), with the goal of assessing and mitigating methodological risks in future studies. Design/methodology/approach This study carries out a systematic literature review of articles published in 11 top-tier SCM journals over a 20-year period. Findings First, while social network research has gained momentum especially since 2010, scholars are not yet entirely aware of the many possibilities the SNA approach offers to the SCM field. Second, expanded possibilities also hold for the development of SNA as a method. Originality/value The paper guides future SCM research by investigating when SNA is the right approach to use and how SNA as a method should be performed. Theoretically richer and practically more relevant research should result.

Vocational Rehabilitation Service Patterns: An Application of Social Network Analysis to Examine Employment Outcomes of Transition-Age Individuals With Autism

2017 ◽  
Vol 61 (3) ◽  
pp. 143-153 ◽  
Author(s):  
Nicole M. Ditchman ◽  
Jennifer L. Miller ◽  
Amanda B. Easton
Keyword(s):  
Young Adults ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Vocational Rehabilitation ◽  
Employment Outcomes ◽  
Autism Spectrum ◽  
Rehabilitation Services ◽  
Data Set ◽  
Service Patterns

Young adults with autism spectrum disorder (ASD) face poor employment outcomes following transition from school to adult life. Social network analysis is a useful approach for examining service patterns associated with employment success for this population. An advantage of this approach is its focus on the interdependence of variables rather than individual predictors. This study applies network methodology to examine the relations between vocational rehabilitation services and young adults with ASD to predict employment status. Using the Rehabilitation Services Administration (RSA-911) data set, participants included 2,219 individuals with ASD between the ages of 16 and 24 served by the public vocational rehabilitation system and closed as either competitively employed or not employed. A two-mode network was constructed such that a relation was defined for each service an individual received. Results from a core-periphery analysis indicated that of the 22 services available, core services included assessment, counseling/guidance, job placement, on-the-job support, job search support, and transportation services. Follow-up analyses suggested that the greater number of these six core services an individual received, the better the employment outcome. Findings highlight that these services should be viewed as interconnected and suggest a set of six core services that may be particularly beneficial for this population.

Data Preprocessing for Dynamic Social Network Analysis

2013 ◽  
pp. 25-39 ◽  
Author(s):  
Preeti Gupta ◽  
Vishal Bhatnagar
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Data Preprocessing ◽  
Original Data ◽  
Network Data ◽  
Data Set ◽  
Social Network Data ◽  
Dynamic Social Network ◽  
The Social

The social network analysis is of significant interest in various application domains due to its inherent richness. Social network analysis like any other data analysis is limited by the quality and quantity of data and for which data preprocessing plays the key role. Before the discovery of useful information or pattern from the social network data set, the original data set must be converted to a suitable format. In this chapter we present various phases of social network data preprocessing. In this context, the authors discuss various challenges in each phase. The goal of this chapter is to illustrate the importance of data preprocessing for social network analysis.

scholarly journals Proximity patterns in water buffaloes' activities on pasture

2020 ◽  
Vol 63 (1) ◽  
pp. 19-29
Author(s):  
Eleni T. Tsiobani ◽  
Maria D. Yiakoulaki ◽  
Nikolaos D. Hasanagas ◽  
Ioannis E. Antoniou
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Structure ◽  
Management Practices ◽  
Local Network ◽  
Age And Gender ◽  
Water Buffaloes ◽  
And Gender ◽  
The Way

Abstract. Water buffaloes are considered social animals and perform several activities on pasture, such as grazing, moving, standing, ruminating, wallowing, lying, and drinking. However, the way these animals form their social structure in the herd during each one of these activities is still unknown. Literature for water buffaloes has focused mainly on their productive characteristics, impact of grazing on wetlands and behavior during grazing but failed to address the way these animals form their social organization during their activities on pasture. In this study, the tools of social network analysis are used to analyze, detect, and depict the proximity patterns in water buffaloes' activities on pasture and the effect of their age and gender on them. We describe and interpret a series of global and local network indices, and show that the water buffaloes differentiate their social structure in their activities on pasture and that the animals' age and gender affect their interacting patterns, and provide a framework for the application of social network analysis on grazing animals' social behavioral studies. We expect that this framework could be used in future research to provide information regarding the social structure of other kinds of animals that graze in different forage and climatic environments and help animal breeders to improve their management practices.

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