A Big Data Platform for Real Time Analysis of Signs of Depression in Social Media

In this paper we propose a scalable platform for real-time processing of Social Media data. The platform ingests huge amounts of contents, such as Social Media posts or comments, and can support Public Health surveillance tasks. The processing and analytical needs of multiple screening tasks can easily be handled by incorporating user-defined execution graphs. The design is modular and supports different processing elements, such as crawlers to extract relevant contents or classifiers to categorise Social Media. We describe here an implementation of a use case built on the platform that monitors Social Media users and detects early signs of depression.

Download Full-text

Real time analysis of social media data to understand people emotions towards national parties

2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT) ◽

10.1109/icccnt.2017.8204059 ◽

2017 ◽

Cited By ~ 2

Author(s):

Santoshi Kuamri ◽

C Narendra Babu

Keyword(s):

Social Media ◽

Real Time ◽

Time Analysis ◽

Social Media Data ◽

Real Time Analysis ◽

Media Data

Download Full-text

The Slandail Monitor: Real-Time Processing and Visualisation of Social Media Data for Emergency Management

2016 11th International Conference on Availability, Reliability and Security (ARES) ◽

10.1109/ares.2016.81 ◽

2016 ◽

Cited By ~ 3

Author(s):

Xiubo Zhang ◽

Stephen Kelly ◽

Khurshid Ahmad

Keyword(s):

Social Media ◽

Emergency Management ◽

Real Time ◽

Social Media Data ◽

Real Time Processing ◽

Time Processing ◽

Media Data

Download Full-text

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

10.2196/preprints.10506 ◽

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.

Download Full-text

Classification Connection of Twitter Data using K-Means Clustering

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.f1004.0486s419 ◽

2019 ◽

Vol 8 (6S4) ◽

pp. 14-22

Keyword(s):

Social Media ◽

Real Time ◽

Clustering Methods ◽

Time Analysis ◽

Data Set ◽

Dynamic Data ◽

Real Time Analysis ◽

Twitter Data ◽

Social Media Platforms ◽

Insight Into

The rise of social media platforms like Twitter and the increasing adoption by people in order to stay connected provide a large source of data to perform analysis based on the various trends, events and even various personalities. Such analysis also provides insight into a person’s likes and inclinations in real time independent of the data size. Several techniques have been created to retrieve such data however the most efficient technique is clustering. This paper provides an overview of the algorithms of the various clustering methods as well as looking at their efficiency in determining trending information. The clustered data may be further classified by topics for real time analysis on a large dynamic data set. In this paper, data classification is performed and analyzed for flaws followed by another classification on the same data set.

Download Full-text