scholarly journals A Secure Based Preserving Social Media Data Mangement System

2021 ◽  
Vol 10 (4) ◽  
pp. 210-214
Author(s):  
Geetha V. ◽  
Gomathy C.K. ◽  
Maddu Pavan Manikanta Kiran ◽  
Rajesh, Gandikota
Keyword(s):  
Social Media ◽  
Main Idea ◽  
Data Publishing ◽  
Activity Data ◽  
Online Activity ◽  
Social Media Data ◽  
Private Data ◽  
User Activity ◽  
Real World Datasets ◽  
Media Data

Personalized suggestions are important to help users find relevant information. It often depends on huge collection of user data, especially users’ online activity (e.g., liking/commenting/sharing) on social media, thereto user interests. Publishing such user activity makes inference attacks easy on the users, as private data (e.g., contact details) are often easily gathered from the users’ activity data. during this module, we proposed PrivacyRank, an adjustable and always protecting privacy on social media data publishing framework , which protects users against frequent attacks while giving personal ranking based recommendations. Its main idea is to continuously blur user activity data like user-specified private data is minimized under a given data budget, which matches round the ranking loss suffer from the knowledge blurring process so on preserve the usage of the info for enabling suggestions. a true world evaluation on both synthetic and real-world datasets displays that our model can provide effective and continuous protection against to the info given by the user, while still conserving the usage of the blurred data for private ranking based suggestion. Compared to other approaches, Privacy Rank achieves both better privacy protection and a far better usage altogether the rank based suggestions use cases we tested.

scholarly journals Characterizing communities of hashtag usage on twitter during the 2020 COVID-19 pandemic by multi-view clustering

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Iain J. Cruickshank ◽  
Kathleen M. Carley
Keyword(s):  
Social Media ◽  
Temporal Trends ◽  
Data Types ◽  
High Quality ◽  
Online Activity ◽  
Social Media Data ◽  
Clustering Technique ◽  
Media Data

Abstract The COVID-19 pandemic has produced a flurry of online activity on social media sites. As such, analysis of social media data during the COVID-19 pandemic can produce unique insights into discussion topics and how those topics evolve over the course of the pandemic. In this study, we propose analyzing discussion topics on Twitter by clustering hashtags. In order to obtain high-quality clusters of the Twitter hashtags, we also propose a novel multi-view clustering technique that incorporates multiple different data types that can be used to describe how users interact with hashtags. The results of our multi-view clustering show that there are distinct temporal and topical trends present within COVID-19 twitter discussion. In particular, we find that some topical clusters of hashtags shift over the course of the pandemic, while others are persistent throughout, and that there are distinct temporal trends in hashtag usage. This study is the first to use multi-view clustering to analyze hashtags and the first analysis of the greater trends of discussion occurring online during the COVID-19 pandemic.

To sell, or not to sell: social media data-breach in second-hand Android devices

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Oussama BenRhouma ◽  
Ali AlZahrani ◽  
Ahmad AlKhodre ◽  
Abdallah Namoun ◽  
Wasim Ahmad Bhat
Keyword(s):  
Social Media ◽  
Social Network ◽  
Use Cases ◽  
Road Accidents ◽  
Data Breach ◽  
Social Media Data ◽  
Content Type ◽  
Private Data ◽  
The Social ◽  
Media Data

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.

scholarly journals Mental Health Computing via Harvesting Social Media Data

2018 ◽  
Author(s):  
Jia Jia
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Mental Health Problems ◽  
Rapid Development ◽  
Health Issues ◽  
Social Media Data ◽  
Mental Health Issues ◽  
Depth Analysis ◽  
Real World Datasets ◽  
Media Data

Mental health has become a general concern of people nowadays. It is of vital importance to detect and manage mental health issues before they turn into severe problems. Traditional psychological interventions are reliable, but expensive and hysteretic. With the rapid development of social media, people are increasingly sharing their daily lives and interacting with friends online. Via harvesting social media data, we comprehensively study the detection of mental wellness, with two typical mental problems, stress and depression, as specific examples. Initializing with binary user-level detection, we expand our research towards multiple contexts, by considering the trigger and level of mental health problems, and involving different social media platforms of different cultures. We construct several benchmark real-world datasets for analysis and propose a series of multi-modal detection models, whose effectiveness are verified by extensive experiments. We also make in-depth analysis to reveal the underlying online behaviors regarding these mental health issues.

Smart De-Identification of Social Media Data

2014 ◽  
Author(s):  
Kathleen M. Carley ◽  
L. R. Carley ◽  
Jonathan Storrick
Keyword(s):  
Social Media ◽  
Social Media Data ◽  
Media Data

Mining Social Media Data to Study the Consequences of Dementia Diagnosis on Caregivers and Relatives (Preprint)

2018 ◽  
Author(s):  
Anika Oellrich ◽  
George Gkotsis ◽  
Richard James Butler Dobson ◽  
Tim JP Hubbard ◽  
Rina Dutta
Keyword(s):  
Social Media ◽  
Family Relationships ◽  
Text Processing ◽  
Automated Analysis ◽  
Health Concern ◽  
Dementia Diagnosis ◽  
Data Set ◽  
Social Media Data ◽  
Real Time Processing ◽  
Media Data

BACKGROUND Dementia is a growing public health concern with approximately 50 million people affected worldwide in 2017 and this number is expected to reach more than 131 million by 2050. The toll on caregivers and relatives cannot be underestimated as dementia changes family relationships, leaves people socially isolated, and affects the finances of all those involved. OBJECTIVE The aim of this study was to explore using automated analysis (i) the age and gender of people who post to the social media forum Reddit about dementia diagnoses, (ii) the affected person and their diagnosis, (iii) relevant subreddits authors are posting to, (iv) the types of messages posted and (v) the content of these posts. METHODS We analysed Reddit posts concerning dementia diagnoses. We used a previously developed text analysis pipeline to determine attributes of the posts as well as their authors to characterise online communications about dementia diagnoses. The posts were also examined by manual curation for the diagnosis provided and the person affected. Furthermore, we investigated the communities these people engage in and assessed the contents of the posts with an automated topic gathering technique. RESULTS Our results indicate that the majority of posters in our data set are women, and it is mostly close relatives such as parents and grandparents that are mentioned. Both the communities frequented and topics gathered reflect not only the sufferer's diagnosis but also potential outcomes, e.g. hardships experienced by the caregiver. The trends observed from this dataset are consistent with findings based on qualitative review, validating the robustness of social media automated text processing. CONCLUSIONS This work demonstrates the value of social media data sources as a resource for in-depth studies of those affected by a dementia diagnosis and the potential to develop novel support systems based on their real time processing in line with the increasing digitalisation of medical care.

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