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

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.

Download Full-text

Social network analysis of children with autism spectrum disorder: Predictors of fragmentation and connectivity in elementary school classrooms

Autism ◽

10.1177/1362361315603568 ◽

2015 ◽

Vol 20 (6) ◽

pp. 700-709 ◽

Cited By ~ 15

Author(s):

Ariana Anderson ◽

Jill Locke ◽

Mark Kretzmann ◽

Connie Kasari ◽

Keyword(s):

Autism Spectrum Disorder ◽

Elementary School ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Children With Autism ◽

Autism Spectrum ◽

Spectrum Disorder

Download Full-text

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

Data Technologies and Applications ◽

10.1108/dta-05-2020-0103 ◽

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.

Download Full-text

Employment Outcomes of Vocational Rehabilitation Clients With Autism Spectrum Disorders

Career Development and Transition for Exceptional Individuals ◽

10.1177/2165143416629366 ◽

2016 ◽

Vol 40 (3) ◽

pp. 144-155 ◽

Cited By ~ 10

Author(s):

Charlotte Y. Alverson ◽

Scott H. Yamamoto

Keyword(s):

Vocational Rehabilitation ◽

Employment Outcomes ◽

Autism Spectrum ◽

Rehabilitation Services ◽

Competitive Employment ◽

Employment Outcome ◽

Adults With Autism ◽

Rehabilitation Services Administration ◽

Spectrum Disorders ◽

The Relationship

Research has consistently documented poor employment outcomes for young adults with autism spectrum disorder (ASD). Vocational rehabilitation (VR) services provide substantial federal and state commitments to individuals with disabilities to obtain and maintain employment. To date, little research has examined the relationship between VR services and employment outcomes of clients with ASD. The purpose of this descriptive study was to better understand employment outcomes of individuals with ASD. Data spanning 10 years from the Rehabilitation Services Administration (RSA) 911 database were analyzed to identify characteristics of VR clients with ASD and the services they received. The percent of individuals who achieved competitive employment averaged 37% across the 10 years. Those who achieved an employment outcome participated in twice as many services as those who did not achieve an employment outcome.

Download Full-text

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

10.2196/preprints.22374 ◽

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.

Download Full-text

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

Journal of Medical Internet Research ◽

10.2196/22374 ◽

2020 ◽

Vol 22 (10) ◽

pp. e22374 ◽

Cited By ~ 3

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.

Download Full-text

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

Clothing and Textiles Research Journal ◽

10.1177/0887302x18821187 ◽

2018 ◽

Vol 37 (2) ◽

pp. 87-102 ◽

Cited By ~ 6

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.

Download Full-text

Effective Job Supports to Improve Employment Outcomes for Individuals With Autism Spectrum Disorder

Journal of Applied Rehabilitation Counseling ◽

10.1891/jarc-d-20-00017 ◽

2021 ◽

pp. JARC-D-20-00017

Author(s):

Kayli Seagraves

Keyword(s):

Autism Spectrum Disorder ◽

Vocational Rehabilitation ◽

Employment Outcomes ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Rehabilitation Counselors ◽

Rehabilitation Services ◽

Competitive Employment ◽

Vocational Rehabilitation Services ◽

Natural Supports

The unemployment rate of those with autism spectrum disorder (ASD) is staggeringly low. The low number of those with ASD in the competitive workforce can be connected to the complex and sometimes severe manifestations of the disorder. However, there are many supports and accommodations that individuals with ASD can use in order to alleviate the stress of finding and maintaining competitive employment. Natural supports can be provided through supportive supervisors, informed coworkers, and on-the-job trainings. Vocational rehabilitation services are provided in order to assess, prepare, and support individuals with ASD through their employment. Lastly, job modifications are used to alleviate any stressors that an employee with ASD may experience on the job. In this literature it was found that natural supports, vocational rehabilitation services, and job modifications were effective in improving the employment outcomes for individuals with ASD. Rehabilitation counselors can use the information found in this literature review to inform employers of successful and effective job supports for employees with ASD.

Download Full-text

Data Preprocessing for Dynamic Social Network Analysis

Data Mining in Dynamic Social Networks and Fuzzy Systems - Advances in Data Mining and Database Management ◽

10.4018/978-1-4666-4213-3.ch002 ◽

2013 ◽

pp. 25-39 ◽

Cited By ~ 1

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.

Download Full-text