A Desktop Usability Evaluation of the Facebook Mobile Interface using the JAWS Screen Reader with Blind Users

Social networking sites (SNSs) like Facebook are widely used and have been broadly studied but despite years of investigation, accessibility complaints from individuals with visual impairments continue to persist. To investigate this issue we have conducted a quasi-ethnographic usability evaluation of Facebook involving blind participants, the mobile interface ( m.facebook.com ) and the JAWS screen reader on a desktop computer; a configuration that has been suggested in the related literature but insufficiently investigated. Six participants attempted 18 tasks designed to be representative of common SNS user activities. Of the features evaluated participants were most severely challenged by the process of creating a user profile and identifying other users with whom to establish relationships; two of the three core activities commonly viewed as characterizing SNSs. These findings suggest that despite recent progress additional research may be needed to make Facebook truly accessible for individuals with visual impairments.

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USING OSINT TO GATHER INFORMATION ABOUT A USER FROM MULTIPLE SOCIAL NETWORKS

INFORMATION TECHNOLOGY IN INDUSTRY ◽

10.17762/itii.v9i2.332 ◽

2021 ◽

Vol 9 (2) ◽

pp. 207-211

Author(s):

Nilesh Sambhe, Piyush Varma, Arpan Adlakhiya, Aditya Mahakalkar, Nihal Nakade, Renuka Lakhe

Keyword(s):

Social Media ◽

Social Networking ◽

Social Networking Sites ◽

User Profile ◽

The Internet ◽

Intelligence Gathering ◽

Open Source Intelligence ◽

Digital Document ◽

Automated Software ◽

Available Information

With the widespread and easier access of the internet, many people have started to use various social networking sites each catering to their needs. It has been observed that most users prefer to use the same social media handle or username on multiple sites for easier management. This makes it possible to get a hold of the publicly available information about the user. But, with the increase in privacy protections and user restrictions, investigators often struggle to gather information about a user. We propose an automated software to perform this job which uses Open-Source Intelligence Gathering (OSINT) methods where all publicly available information of a user is gathered in an intelligently structured format all at one place. The software will search various social networking sites for the required user profile and gather all publicly available information. This information will then be available to investigators with the facility to export in various digital document formats.

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Usability evaluation of two social networking sites

PsycEXTRA Dataset ◽

10.1037/e578802012-015 ◽

2010 ◽

Author(s):

Javier Rivera ◽

Fleet Davis ◽

Mustapha Mouloua ◽

Pascal Alberti

Keyword(s):

Social Networking ◽

Social Networking Sites ◽

Usability Evaluation

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Semantic matching of job seeker to vacancy: a bidirectional approach

International Journal of Manpower ◽

10.1108/ijm-10-2018-0331 ◽

2018 ◽

Vol 39 (8) ◽

pp. 1047-1063 ◽

Cited By ~ 2

Author(s):

Sisay Adugna Chala ◽

Fazel Ansari ◽

Madjid Fathi ◽

Kea Tijdens

Keyword(s):

Social Networking ◽

Social Networking Sites ◽

User Profile ◽

Machine Learning Techniques ◽

Web Survey ◽

Semantic Matching ◽

Job Seekers ◽

Content Type ◽

Individual Level ◽

Occupational Standards

Purpose The purpose of this paper is to propose a framework of an automatic bidirectional matching system that measures the degree of semantic similarity of job-seeker qualifications and skills, against the vacancy provided by employers or job-agents. Design/methodology/approach The paper presents a framework of bidirectional jobseeker-to-vacancy matching system. Using occupational data from various sources such as the WageIndicator web survey, International Standard Classification of Occupations, European Skills, Competences, Qualifications, and Occupations as well as vacancy data from various open access internet sources and job seekers information from social networking sites, the authors apply machine learning techniques for bidirectional matching of job vacancies and occupational standards to enhance the contents of job vacancies and job seekers profiles. The authors also apply bidirectional matching of job seeker profiles and vacancies, i.e., semantic matching vacancies to job seekers and vice versa in the individual level. Moreover, data from occupational standards and social networks were utilized to enhance the relevance (i.e. degree of similarity) of job vacancies and job seekers, respectively. Findings The paper provides empirical insights of increase in job vacancy advertisements on the selected jobs – Internet of Things – with respect to other job vacancies, and identifies the evolution of job profiles and its effect on job vacancies announcements in the era of Industry 4.0. In addition, the paper shows the gap between job seeker interests and available jobs in the selected job area. Research limitations/implications Due to limited data about jobseekers, the research results may not guarantee high quality of recommendation and maturity of matching results. Therefore, further research is required to test if the proposed system works for other domains as well as more diverse data sets. Originality/value The paper demonstrates how online jobseeker-to-vacancy matching can be improved by use of semantic technology and the integration of occupational standards, web survey data, and social networking data into user profile collection and matching.

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