scholarly journals Throw me a lifeline: Articulating mobile social network dispersion and the social construction of risk in rescue communication

2019 ◽  
Vol 8 (2) ◽  
pp. 149-169 ◽  
Author(s):  
Keri K. Stephens ◽  
Brett W. Robertson ◽  
Dhiraj Murthy

This research develops a model of mobile social network dispersion in rescue communication, and illustrates how people use a combination of mobile and social media, along with real-time communication, in their decision-making process. Guided by established research on smartphones, social media, and affordances, we used a qualitative approach and conducted field interviews that included photo-elicitation interview (PEI) techniques to examine participants’ private social media data. Our analysis of these rescue decisions reveals why so few people used the official 9-1-1 system. We show how rescue communication often occurs through a socially constructed assessment of risk that involves persuasion by trusted others in their network, regardless of professional qualifications. Furthermore, trusted others can function as proxies and can draw upon mobile social network affordances, helping to compensate for material limitations. The affordances people drew from can be organized into two sets: foundational and amplification. Hierarchical relationships exist among these sets of affordances, and materiality plays a pivotal role in rescue communication. Ultimately, our analysis uncovers the multimodality around people’s decisions to ask for help.

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Oussama BenRhouma ◽  
Ali AlZahrani ◽  
Ahmad AlKhodre ◽  
Abdallah Namoun ◽  
Wasim Ahmad Bhat

Purpose The purpose of this paper is to investigate the private-data pertaining to the interaction of users with social media applications that can be recovered from second-hand Android devices. Design/methodology/approach This study uses a black-box testing-principles based methodology to develop use-cases that simulate real-world case-scenarios of the activities performed by the users on the social media application. The authors executed these use-cases in a controlled experiment and examined the Android smartphone to recover the private-data pertaining to these use-cases. Findings The results suggest that the social media data recovered from Android devices can reveal a complete timeline of activities performed by the user, identify all the videos watched, uploaded, shared and deleted by the user, disclose the username and user-id of the user, unveil the email addresses used by the user to download the application and share the videos with other users and expose the social network of the user on the platform. Forensic investigators may find this data helpful in investigating crimes such as cyber bullying, racism, blasphemy, vehicle thefts, road accidents and so on. However, this data-breach in Android devices is a threat to user's privacy, identity and profiling in second-hand market. Practical implications Perceived notion of data sanitisation as a result of application removal and factory-reset can have serious implications. Though being helpful to forensic investigators, it leaves the user vulnerable to privacy breach, identity theft, profiling and social network revealing in second-hand market. At the same time, users' sensitivity towards data-breach might compel users to refrain from selling their Android devices in second-hand market and hamper device recycling. Originality/value This study attempts to bridge the literature gap in social media data-breach in second-hand Android devices by experimentally determining the extent of the breach. The findings of this study can help digital forensic investigators in solving crimes such as vehicle theft, road accidents, cybercrimes and so on. It can assist smartphone users to decide whether to sell their smartphones in a second-hand market, and at the same time encourage developers and researchers to design methods of social media data sanitisation.


Author(s):  
Mohamad Hasan

This paper presents a model to collect, save, geocode, and analyze social media data. The model is used to collect and process the social media data concerned with the ISIS terrorist group (the Islamic State in Iraq and Syria), and to map the areas in Syria most affected by ISIS accordingly to the social media data. Mapping process is assumed automated compilation of a density map for the geocoded tweets. Data mined from social media (e.g., Twitter and Facebook) is recognized as dynamic and easily accessible resources that can be used as a data source in spatial analysis and geographical information system. Social media data can be represented as a topic data and geocoding data basing on the text of the mined from social media and processed using Natural Language Processing (NLP) methods. NLP is a subdomain of artificial intelligence concerned with the programming computers to analyze natural human language and texts. NLP allows identifying words used as an initial data by developed geocoding algorithm. In this study, identifying the needed words using NLP was done using two corpora. First corpus contained the names of populated places in Syria. The second corpus was composed in result of statistical analysis of the number of tweets and picking the words that have a location meaning (i.e., schools, temples, etc.). After identifying the words, the algorithm used Google Maps geocoding API in order to obtain the coordinates for posts.


Author(s):  
Carson K.-S. Leung ◽  
Irish J. M. Medina ◽  
Syed K. Tanbeer

The emergence of Web-based communities and social networking sites has led to a vast volume of social media data, embedded in which are rich sets of meaningful knowledge about the social networks. Social media mining and social network analysis help to find a systematic method or process for examining social networks and for identifying, extracting, representing, and exploiting meaningful knowledge—such as interdependency relationships among social entities in the networks—from the social media. This chapter presents a system for analyzing the social networks to mine important groups of friends in the networks. Such a system uses a tree-based mining approach to discover important friend groups of each social entity and to discover friend groups that are important to social entities in the entire social network.


2020 ◽  
Vol 111 ◽  
pp. 819-828 ◽  
Author(s):  
Joseph T. Yun ◽  
Nickolas Vance ◽  
Chen Wang ◽  
Luigi Marini ◽  
Joseph Troy ◽  
...  

2018 ◽  
Vol 7 (4.38) ◽  
pp. 939
Author(s):  
Nur Atiqah Sia Abdullah ◽  
Hamizah Binti Anuar

Facebook and Twitter are the most popular social media platforms among netizen. People are now more aggressive to express their opinions, perceptions, and emotions through social media platforms. These massive data provide great value for the data analyst to understand patterns and emotions related to a certain issue. Mining the data needs techniques and time, therefore data visualization becomes trending in representing these types of information. This paper aims to review data visualization studies that involved data from social media postings. Past literature used node-link diagram, node-link tree, directed graph, line graph, heatmap, and stream graph to represent the data collected from the social media platforms. An analysis by comparing the social media data types, representation, and data visualization techniques is carried out based on the previous studies. This paper critically discussed the comparison and provides a suggestion for the suitability of data visualization based on the type of social media data in hand.      


2019 ◽  
Vol 10 (2) ◽  
pp. 57-70 ◽  
Author(s):  
Vikas Kumar ◽  
Pooja Nanda

With the amplification of social media platforms, the importance of social media analytics has exponentially increased for many brands and organizations across the world. Tracking and analyzing the social media data has been contributing as a success parameter for such organizations, however, the data is being poorly harnessed. Therefore, the ethical implications of social media analytics need to be identified and explored for both the organizations and targeted users of social media data. The present work is an exploratory study to identify the various techno-ethical concerns of social media engagement, as well as social media analytics. The impact of these concerns on the individuals, organizations, and society as a whole are discussed. Ethical engagement for the most common social media platforms has been outlined with a number of specific examples to understand the prominent techno-ethical concerns. Both the individual and organizational perspectives have been taken into account to identify the implications of social media analytics.


2018 ◽  
Vol 45 (1) ◽  
pp. 136-136

Ji X, Chun SA, Cappellari P, et al. Linking and using social media data for enhancing public health analytics. Journal of Information Science 2016; 43: 221–245. DOI: 10.1177/0165551515625029 The authors regret that non-anonymised patient data was used from a social medical network without prior permission. With permission from the social medical network, the authors have anonymised the data and corrected the article. The online version of the article has been corrected.


Sign in / Sign up

Export Citation Format

Share Document