Taxonomy of Community Detection over Social Media

Advancements in web technologies in conjunction with the advent of social media facilitate online users to share contents and interact on a shared platform. Social media mining allows users to visualize, evaluate, analyze, and extract meaningful patterns and trends over the social network. Numerous methods and algorithms have been presented for the massive investigation of social media data. Community detection over social media is the most attracting field of interest for researchers in the area of social media mining. Community detection is a process of identifying densely connected network nodes and forming a group or community based on the density of interconnection among them. Detection of such communities is very crucial for a variety of applications in order to analyze the social network. This paper provides a brief introduction of social media, social media mining, and highlights prominent and recent research works done in the field of community detection. The paper presents the taxonomy of various algorithms and approaches for community detection over social media. The paper also includes in-depth details of extent community detection methods devised in the literature to detect communities over social media.

Download Full-text

Analyzing Social Networks to Mine Important Friends

Social Media Mining and Social Network Analysis ◽

10.4018/978-1-4666-2806-9.ch006 ◽

2013 ◽

pp. 90-104 ◽

Cited By ~ 18

Author(s):

Carson K.-S. Leung ◽

Irish J. M. Medina ◽

Syed K. Tanbeer

Keyword(s):

Social Networks ◽

Social Media ◽

Social Network ◽

Social Networking Sites ◽

Web Based ◽

The Social ◽

Social Entity ◽

Social Entities ◽

Media Data ◽

Media Mining

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.

Download Full-text

Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocy114 ◽

2018 ◽

Vol 25 (10) ◽

pp. 1274-1283 ◽

Cited By ~ 20

Author(s):

Abeed Sarker ◽

Maksim Belousov ◽

Jasper Friedrichs ◽

Kai Hakala ◽

Svetlana Kiritchenko ◽

...

Keyword(s):

Social Media ◽

Text Classification ◽

Training Data ◽

Support Vector ◽

Shared Task ◽

Social Media Mining ◽

The Social ◽

Related Text ◽

Health Related ◽

Media Mining

AbstractObjectiveWe executed the Social Media Mining for Health (SMM4H) 2017 shared tasks to enable the community-driven development and large-scale evaluation of automatic text processing methods for the classification and normalization of health-related text from social media. An additional objective was to publicly release manually annotated data.Materials and MethodsWe organized 3 independent subtasks: automatic classification of self-reports of 1) adverse drug reactions (ADRs) and 2) medication consumption, from medication-mentioning tweets, and 3) normalization of ADR expressions. Training data consisted of 15 717 annotated tweets for (1), 10 260 for (2), and 6650 ADR phrases and identifiers for (3); and exhibited typical properties of social-media-based health-related texts. Systems were evaluated using 9961, 7513, and 2500 instances for the 3 subtasks, respectively. We evaluated performances of classes of methods and ensembles of system combinations following the shared tasks.ResultsAmong 55 system runs, the best system scores for the 3 subtasks were 0.435 (ADR class F1-score) for subtask-1, 0.693 (micro-averaged F1-score over two classes) for subtask-2, and 88.5% (accuracy) for subtask-3. Ensembles of system combinations obtained best scores of 0.476, 0.702, and 88.7%, outperforming individual systems.DiscussionAmong individual systems, support vector machines and convolutional neural networks showed high performance. Performance gains achieved by ensembles of system combinations suggest that such strategies may be suitable for operational systems relying on difficult text classification tasks (eg, subtask-1).ConclusionsData imbalance and lack of context remain challenges for natural language processing of social media text. Annotated data from the shared task have been made available as reference standards for future studies (http://dx.doi.org/10.17632/rxwfb3tysd.1).

Download Full-text

Social Network Mental Disorders Detection via Online Social Media Mining

International Journal for Modern Trends in Science and Technology - RTT2020 ◽

10.46501/ijmtst0701006 ◽

2021 ◽

Vol 7 (01) ◽

pp. 27-30

Author(s):

Prof. Narinder Kaur and Lakshay Monga

Keyword(s):

Social Media ◽

Social Network ◽

Mental Disorders ◽

Mental Disorder ◽

Sentiment Analysis ◽

Prototype System ◽

Social Media Mining ◽

Online Social Media ◽

Analyse Data ◽

Media Mining

Social Network Mental Disorder Detection” or “SNMD” is an approach to analyse data and retrieve sentiment that it embodies. Twitter SNMD analysis is an application of sentiment analysis on data from Twitter (tweets), in order to extract sentiments conveyed by the user. In this paper, we aim to review some papers regarding research in sentiment analysis on Twitter, describing the methodologies adopted and models applied, along with describing a generalized Python based approach. A prototype system is developed and tested.

Download Full-text