Analyzing big data in social media: Text and network analyses of an eating disorder forum

2018 ◽  
Vol 51 (7) ◽  
pp. 656-667 ◽  
Author(s):  
Markus Moessner ◽  
Johannes Feldhege ◽  
Markus Wolf ◽  
Stephanie Bauer
Keyword(s):  
Social Media ◽  
Big Data ◽  
Eating Disorder ◽  
Network Analyses ◽  
Social Media Text

scholarly journals A study on Predictive Modeling of Users’ Parasocial Relationship Types based on Social Media Text Big Data

2022 ◽  
Vol 16 ◽  
pp. 171-180
Author(s):  
Jiatong Meng ◽  
Yucheng Chen
Keyword(s):  
Social Media ◽  
Big Data ◽  
Prediction Model ◽  
Social Relationship ◽  
Performance Test ◽  
Semantic Analysis ◽  
Processing Efficiency ◽  
Relationship Type ◽  
Social Media Text ◽  
User Data

The traditional quasi-social relationship type prediction model obtains prediction results by analyzing and clustering the direct data. The prediction results are easily disturbed by noisy data, and the problems of low processing efficiency and accuracy of the traditional prediction model gradually appear as the amount of user data increases. To address the above problems, the research constructs a prediction model of user quasi-social relationship type based on social media text big data. After pre-processing the collected social media text big data, the interference data that affect the accuracy of non-model prediction are removed. The interaction information in the text data is mined based on the principle of similarity calculation, and semantic analysis and sentiment annotation are performed on the information content. On the basis of BP neural network, we construct a prediction model of user’s quasi-social relationship type. The performance test data of the model shows that the average prediction accuracy of the constructed model is 89.84%, and the model has low time complexity and higher processing efficiency, which is better than other traditional models.

Daily and hourly mood pattern discovery of Turkish twitter users

2015 ◽  
Vol 5 (2) ◽  
pp. 90
Author(s):  
Mete Celik ◽  
Ahmet Sakir Dokuz
Keyword(s):  
Social Media ◽  
Big Data ◽  
Support Vector ◽  
Feature Selection Technique ◽  
Social Media Data ◽  
Web Technologies ◽  
Document Frequency ◽  
Social Media Text ◽  
Twitter Users ◽  
Media Data

<p>Massive amount of data-related applications and widespread usage of web technologies has started big data era. Social media data is one of the big data sources. Mining social media data provides useful insights for companies and organizations for developing their services, products or organizations. This study aims to analyze Turkish Twitter users based on daily and hourly social media sharings. By this way, daily and hourly mood patterns of Turkish social media users could be revealed in positive or negative manner. For this purpose, Support Vector Machines (SVM) classification algorithm and Term Frequency – Inverse Document Frequency (TF-IDF) feature selection technique was used. As far as our knowledge, this is the first attempt to analyze people’s all sharings on social media and generate results for temporal-based indicators like macro and micro levels.</p><p> </p><p>Keywords: big data, social media, text classification, svm, tf-idf term weighting, daily and hourly mood patterns.</p>

scholarly journals Real Time Traffic Prediction Based On Social Media Text Data Using Deep Learning

2021 ◽  
Author(s):  
B. Mounica ◽  
K. Lavanya
Keyword(s):  
Social Media ◽  
Big Data ◽  
Real Time ◽  
Traffic Management ◽  
Traffic Analysis ◽  
Traffic Prediction ◽  
Text Data ◽  
Real Time Traffic ◽  
Data Architecture ◽  
Social Media Text

Due to urbanization Traffic management is one of the major issues in contemporary civic management, considering this circumstance traffic analysis is turning into the need of the present world. Text data generated by Twitter, Facebook and other social media platforms can be used for traffic management. Big data helps in traffic prediction and traffic analysis of advancing metropolitan zones. Constant traffic investigation requires preparing of information streams that are produced persistently to increase fast experiences. To measures stream information at a fast rate advancements on high figuring limit is required. Social media text data can be processed by using batch processing and stream processing with big data architecture through Spark and Hadoop framework. In this paper big data architecture is proposed for real time traffic text data analysis. In architecture Spark and Kafka are used in combination. Kafka helps in pipelines text data used in conjunction with spark stream processing engine. Big data architecture using Spark, Kafka with ability for processing and preparing huge measure of information, have settled the serious issue of handling and putting away constantly streaming data. The traffic information from Twitter API is streamed. In The proposed model pointed toward ensemble neural network model to reduce the variance in results for better prediction foreseeing traffic stream text data by incorporating Spark and Kafka that will be of an extraordinary incentive to the public authority for traffic management and analysis.

Modeling Factuality Judgments in Social Media Text

2014 ◽  
Author(s):  
Sandeep Soni ◽  
Tanushree Mitra ◽  
Eric Gilbert ◽  
Jacob Eisenstein
Keyword(s):  
Social Media ◽  
Social Media Text

DYNAMIC EXTRACTION AND ANALYTICS OF BIG DATA FROM CLOUD AND SOCIAL MEDIA INTEGRATED PLATFORMS

2020 ◽  
Vol 9 (6) ◽  
pp. 3703-3711
Author(s):  
N. Oberoi ◽  
S. Sachdeva ◽  
P. Garg ◽  
R. Walia
Keyword(s):  
Social Media ◽  
Big Data ◽  
Dynamic Extraction

Citizens, Elites, and Social Media Methodological Challenges and Opportunities in the Study of Persuasion and Mobilization

2019 ◽  
pp. 1036-1058
Author(s):  
Philip Habel ◽  
Yannis Theocharis
Keyword(s):  
Social Media ◽  
Big Data ◽  
Supply And Demand ◽  
Political Process ◽  
The Political ◽  
The Novel ◽  
Complete Understanding ◽  
Social Media Data ◽  
Challenges And Opportunities ◽  
Media Data

In the last decade, big data, and social media in particular, have seen increased popularity among citizens, organizations, politicians, and other elites—which in turn has created new and promising avenues for scholars studying long-standing questions of communication flows and influence. Studies of social media play a prominent role in our evolving understanding of the supply and demand sides of the political process, including the novel strategies adopted by elites to persuade and mobilize publics, as well as the ways in which citizens react, interact with elites and others, and utilize platforms to persuade audiences. While recognizing some challenges, this chapter speaks to the myriad of opportunities that social media data afford for evaluating questions of mobilization and persuasion, ultimately bringing us closer to a more complete understanding Lasswell’s (1948) famous maxim: “who, says what, in which channel, to whom, [and] with what effect.”

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