scholarly journals Exploring differences in perceptions around Social Media Competencies: An expert vs. frontline user study

2021 ◽  
Author(s):  
Yusuf YILMAZ ◽  
Puru PANCHAL ◽  
Jessica G. Y. LUC ◽  
Ali RAJA ◽  
Brent THOMA ◽  
...  
Keyword(s):  
Social Media ◽  
User Study

scholarly journals Verbal Aggression on Social Media: How, why and its Automatic Identification

2021 ◽  
Author(s):  
Ritesh Kumar
Keyword(s):  
Social Media ◽  
Quantitative Study ◽  
Large Scale ◽  
User Study ◽  
Hate Speech ◽  
Verbal Aggression ◽  
User Studies ◽  
Automatic Identification ◽  
System P ◽  
Overall Performance

In recent times, verbal aggression and related phenomena of hate speech, abusive language, trolling, etc. have become a major problem over social media. In this paper, I present the results of a large-scale quantitative study of aggression based on a target-based typology in a manually-annotated multilingual dataset of over 20,000 Facebook comments and tweets each written in Hindi, English or code-mixed Hindi-English. Taking insights from this study, I develop 2 different classifiers for detecting aggression in Hindi, English and Hindi-English mixed Facebook and Twitter conversations. The classifiers are developed using an annotatedcorpus of approximately 9,000 Facebook comments and 5,000 tweets. Since a phenomenon like aggression is highly subjective, the study shows a comparatively modest inter-annotator agreement of 0.72 and an overall F1 score of 0.64 for both Facebook and Twitter. Consequently, I also carried out two user studies, where humans were asked to evaluate the annotations by the classifier, to test the actual 'acceptance' of the classifier's judgments. I discuss the results of this user study and give an analysis of the overall performance of the system.

scholarly journals Face-Off: Adversarial Face Obfuscation

2021 ◽  
Vol 2021 (2) ◽  
pp. 369-390
Author(s):  
Varun Chandrasekaran ◽  
Chuhan Gao ◽  
Brian Tang ◽  
Kassem Fawaz ◽  
Somesh Jha ◽  
...  
Keyword(s):  
Social Media ◽  
Face Recognition ◽  
Social Interactions ◽  
User Study ◽  
Service Providers ◽  
Black Box ◽  
Trade Off ◽  
Privacy Threats ◽  
Social Media Platforms ◽  
Privacy Risks

Abstract Advances in deep learning have made face recognition technologies pervasive. While useful to social media platforms and users, this technology carries significant privacy threats. Coupled with the abundant information they have about users, service providers can associate users with social interactions, visited places, activities, and preferences–some of which the user may not want to share. Additionally, facial recognition models used by various agencies are trained by data scraped from social media platforms. Existing approaches to mitigate associated privacy risks result in an imbalanced trade-off between privacy and utility. In this paper, we address this trade-off by proposing Face-Off, a privacy-preserving framework that introduces strategic perturbations to images of the user’s face to prevent it from being correctly recognized. To realize Face-Off, we overcome a set of challenges related to the black-box nature of commercial face recognition services, and the scarcity of literature for adversarial attacks on metric networks. We implement and evaluate Face-Off to find that it deceives three commercial face recognition services from Microsoft, Amazon, and Face++. Our user study with 423 participants further shows that the perturbations come at an acceptable cost for the users.

scholarly journals Automating Dynamic Consent Decisions for the Processing of Social Media Data in Health Research

2019 ◽  
Vol 15 (3) ◽  
pp. 187-201
Author(s):  
Chris Norval ◽  
Tristan Henderson
Keyword(s):  
Social Media ◽  
Health Research ◽  
User Study ◽  
Machine Learning Algorithms ◽  
Middle Ground ◽  
Social Media Data ◽  
Dynamic Consent ◽  
Significant Burden ◽  
Health Related ◽  
Media Data

Social media have become a rich source of data, particularly in health research. Yet, the use of such data raises significant ethical questions about the need for the informed consent of those being studied. Consent mechanisms, if even obtained, are typically broad and inflexible, or place a significant burden on the participant. Machine learning algorithms show much promise for facilitating a “middle-ground” approach: using trained models to predict and automate granular consent decisions. Such techniques, however, raise a myriad of follow-on ethical and technical considerations. In this article, we present an exploratory user study ( n = 67) in which we find that we can predict the appropriate flow of health-related social media data with reasonable accuracy, while minimizing undesired data leaks. We then attempt to deconstruct the findings of this study, identifying and discussing a number of real-world implications if such a technique were put into practice.

Investigating the Effects of Social Media Usage on Sleep Quality

2017 ◽  
Vol 61 (1) ◽  
pp. 1327-1330
Author(s):  
Dinesh Kaimal ◽  
Ravi Teja Sajja ◽  
Farzan Sasangohar
Keyword(s):  
Social Media ◽  
Young Adults ◽  
Sleep Quality ◽  
Sleep Disturbance ◽  
Sample Size ◽  
User Study ◽  
Graduate Course ◽  
Media Usage

Social media usage is widespread among young adults. While the effects of social media usage on depression have been documented, studies to investigate the effects on sleep quality are largely absent. As part of the requirements for a graduate course at Texas A&M University, a user study was conducted to investigate whether usage of social media before bed time would result in sleep disturbance and diminished sleep quality. Ten participants were asked to not use social media before bed (baseline) for one week and use several popular applications for three weeks. While the effects were not statistically significant, social media usage before sleep might still negatively affect sleep quality. Further research with an increased sample size conducted over a longer period is warranted to investigate such effects.

Where Social Media Meets AAC

ASHA Leader ◽  
2015 ◽  
Vol 20 (7) ◽  
Author(s):  
Vicki Clarke
Keyword(s):  
Social Media

Making Twitter Work For You

ASHA Leader ◽  
2013 ◽  
Vol 18 (5) ◽  
Keyword(s):  
Social Media ◽  
Public Relations ◽  
Speech Language Pathologists

As professionals who recognize and value the power and important of communications, audiologists and speech-language pathologists are perfectly positioned to leverage social media for public relations.

Doctors employ social media

2013 ◽  
Vol 44 (1) ◽  
pp. 4
Author(s):  
Jane Anderson
Keyword(s):  
Social Media

A Lesson in Taking Social Media to the Medical Level

2011 ◽  
Vol 44 (7) ◽  
pp. 75
Author(s):  
SALLY KOCH KUBETIN
Keyword(s):  
Social Media
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