Information disclosure of social media users

Benson, Vladlena; Saridakis, George; Tennakoon, Hemamaali

doi:10.1108/itp-10-2014-0232

Cited by 98 publications

(38 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Various studies have been conducted to study check-in behavior under different perspectives like privacy [27,28], gender differences [29], and geographical distances [30]. Research [31,32] has found that the capacity of sharing information with millions of users is a simple method to meet with friends, make new friends, experience new things, and manage one's identity.…”

Section: Related Workmentioning

confidence: 99%

Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

Muhammad

Wan

Cervantes

et al. 2018

IJGI

View full text Add to dashboard Cite

Population density and distribution of services represents the growth and demographic shift of the cities. For urban planners, population density and check-in behavior in space and time are vital factors for planning and development of sustainable cities. Location-based social network (LBSN) data seems to be a complement to many traditional methods (i.e., survey, census) and is used to study check-in behavior, human mobility, activity analysis, and social issues within a city. This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on gender difference and frequency of using LBSN. Therefore, in this study, we investigate the check-in behavior of Chinese microblog Sina Weibo (referred as "Weibo") in 10 districts of Shanghai, China, for which we observe the gender difference and their frequency of use over a period. The mentioned districts were spatially analyzed for check-in spots by kernel density estimation (KDE) using ArcGIS. Furthermore, our results reveal that female users have a high rate of social media use, and significant difference is observed in check-in behavior during weekdays and weekends in the studied districts of Shanghai. Increase in check-ins is observed during the night as compared to the morning. From the results, it can be assumed that LBSN data can be helpful to observe gender difference.

show abstract

Section: Related Workmentioning

confidence: 99%

Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

Muhammad

Wan

Cervantes

et al. 2018

IJGI

View full text Add to dashboard Cite

show abstract

“…Various studies have been conducted to study check-in behavior under different perspectives such as privacy [32,33], gender differences [34], and geographical distances [7]. However, Benevenuto et al [35] presented an analysis of user workloads in online social networks to study opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems.…”

Section: Related Workmentioning

confidence: 99%

Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data

Muhammad

Wan

2018

Information

View full text Add to dashboard Cite

With rapid advancement in location-based services (LBS), their acquisition has become a powerful tool to link people with similar interests across long distances, as well as connecting family and friends. To observe human behavior towards using social media, it is essential to understand and measure the check-in behavior towards a location-based social network (LBSN). This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on the frequency of using an LBSN. In this paper, we investigate the check-in behavior of several million individuals, for whom we observe the gender and their frequency of using Chinese microblog Sina Weibo (referred as “Weibo”) over a period in Shanghai, China. To produce a smooth density surface of check-ins, we analyze the overall spatial patterns by using the kernel density estimation (KDE) by using ArcGIS. Furthermore, our results reveal that female users are more inclined towards using social media, and a difference in check-in behavior during weekday and weekend is also observed. From the results, LBSN data seems to be a complement to traditional methods (i.e., survey, census) and is used to study gender-based check-in behavior.

show abstract

“…People are increasingly using online social networks (or social media 1 ), such as Facebook, Twitter and LinkedIn. However, information-sharing by social-network users can result in violations of privacy (Garg & Camp, 2015) and security (Benson, Saridakis & Tennakoon, 2015). For example, a user whose contact details have been revealed may become the subject of harassment in a deliberate, repeated, and hostile manner (cyber-bullying) or become a potential victim of identity theft.…”

Section: Introductionmentioning

confidence: 99%

Security and privacy in online social networking: Risk perceptions and precautionary behaviour

Schaik

Jansen

Onibokun³

et al. 2018

Computers in Human Behavior

View full text Add to dashboard Cite

A quantitative behavioural online study examined a set of hazards that correspond with security-and privacy settings of the major global online social network (Facebook). These settings concern access to a user's account and access to the user's shared information (both security) as well as regulation of the user's information-sharing and user's regulation of others' information-sharing in relation to the user (both privacy). We measured 201 non-student UK users' perceptions of risk and other risk dimensions, and precautionary behaviour. First, perceptions of risk and dread were highest and precautionary behaviour was most common for hazards related to users' regulation of information-sharing. Other hazards were perceived as less risky and less precaution was taken against these, even though they can lead to breaches of users' security or privacy. Second, consistent with existing theory, significant predictors of perceived risk were attitude towards sharing information on Facebook, dread, voluntariness, catastrophic potential and Internet experience; and significant predictors of precautionary behaviour were perceived risk, control, voluntariness and Internet experience. Methodological implications emphasise the need for non-aggregated analysis and practical implications emphasise interventions to promote safe online social-network use.

show abstract

Information disclosure of social media users

Cited by 98 publications

References 64 publications

Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data

Security and privacy in online social networking: Risk perceptions and precautionary behaviour

Contact Info

Product

Resources

About