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.

scholarly journals Making Inferences Using Incidentally Collected Data

2016 ◽  
Author(s):  
Jonathan Mellon
Keyword(s):  
Social Media ◽  
Big Data ◽  
The Internet ◽  
User Interactions ◽  
Social Media Data ◽  
Broad Definition ◽  
User Data ◽  
The Cost ◽  
Media Data

This chapter discusses the use of large quantities of incidentallycollected data (ICD) to make inferences about politics. This type of datais sometimes referred to as “big data” but I avoid this term because of itsconflicting definitions (Monroe, 2012; Ward & Barker, 2013). ICD is datathat was created or collected primarily for a purpose other than analysis.Within this broad definition, this chapter focuses particularly on datagenerated through user interactions with websites. While ICD has beenaround for at least half a century, the Internet greatly expanded theavailability and reduced the cost of ICD. Examples of ICD include data onInternet searches, social media data, and user data from civic platforms.This chapter briefly explains some sources and uses of ICD and thendiscusses some of the potential issues of analysis and interpretation thatarise when using ICD, including the different approaches to inference thatresearchers can use.

The Use of Social Media for Urban Planning

2019 ◽  
pp. 1049-1070
Author(s):  
Fabian Neuhaus
Keyword(s):  
Social Media ◽  
Big Data ◽  
Urban Areas ◽  
Large Scale ◽  
Urban Environments ◽  
Data Systems ◽  
User Management ◽  
Social Nature ◽  
User Data ◽  
Use Of Social Media

User data created in the digital context has increasingly been of interest to analysis and spatial analysis in particular. Large scale computer user management systems such as digital ticketing and social networking are creating vast amount of data. Such data systems can contain information generated by potentially millions of individuals. This kind of data has been termed big data. The analysis of big data can in its spatial but also in a temporal and social nature be of much interest for analysis in the context of cities and urban areas. This chapter discusses this potential along with a selection of sample work and an in-depth case study. Hereby the focus is mainly on the use and employment of insight gained from social media data, especially the Twitter platform, in regards to cities and urban environments. The first part of the chapter discusses a range of examples that make use of big data and the mapping of digital social network data. The second part discusses the way the data is collected and processed. An important section is dedicated to the aspects of ethical considerations. A summary and an outlook are discussed at the end.

The Use of Social Media for Urban Planning

2014 ◽  
pp. 113-134 ◽  
Author(s):  
Fabian Neuhaus
Keyword(s):  
Social Media ◽  
Big Data ◽  
Urban Areas ◽  
Large Scale ◽  
Urban Environments ◽  
Data Systems ◽  
User Management ◽  
Social Nature ◽  
User Data ◽  
Use Of Social Media

User data created in the digital context has increasingly been of interest to analysis and spatial analysis in particular. Large scale computer user management systems such as digital ticketing and social networking are creating vast amount of data. Such data systems can contain information generated by potentially millions of individuals. This kind of data has been termed big data. The analysis of big data can in its spatial but also in a temporal and social nature be of much interest for analysis in the context of cities and urban areas. This chapter discusses this potential along with a selection of sample work and an in-depth case study. Hereby the focus is mainly on the use and employment of insight gained from social media data, especially the Twitter platform, in regards to cities and urban environments. The first part of the chapter discusses a range of examples that make use of big data and the mapping of digital social network data. The second part discusses the way the data is collected and processed. An important section is dedicated to the aspects of ethical considerations. A summary and an outlook are discussed at the end.

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 Concerns of Thalassemia Patients, Carriers, and their Caregivers in Malaysia: Text Mining Information Shared on Social Media

2021 ◽  
Vol 27 (3) ◽  
pp. 200-213
Author(s):  
Yuen Chi Phang ◽  
Azleena Mohd Kassim ◽  
Ernest Mangantig
Keyword(s):  
Social Media ◽  
Text Mining ◽  
English Language ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Topic Model ◽  
Health Related ◽  
Social Media Text ◽  
Insight Into ◽  
New Framework

Objectives: The main aim of this study was to use text mining on social media to analyze information and gain insight into the health-related concerns of thalassemia patients, thalassemia carriers, and their caregivers.Methods: Posts from two Facebook groups whose members consisted of thalassemia patients, thalassemia carriers, and caregivers in Malaysia were extracted using the Data Miner tool. In this study, a new framework known as Malay-English social media text pre-processing was proposed for performing the steps of pre-processing the noisy mixed language (Malay-English language) of social media posts. Topic modeling was used to identify hidden topics within posts shared among members. Three different topic models—latent Dirichlet allocation (LDA) in GenSim, LDA in MALLET, and latent semantic analysis—were applied to the dataset with and without stemming using Python.Results: LDA in MALLET without stemming was found to be the best topic model for this dataset. Eight topics were identified within the posts shared by members. Of those eight topics, four were newly discovered by this study, and four others corresponded to the findings of previous studies that used an interview approach.Conclusions: Topic 2 (the challenges faced by thalassemia patients) was found to be the topic with the highest attention and engagement. Healthcare practitioners and other concerned parties should make an effort to build a stronger support system related to this issue for those affected by thalassemia.

scholarly journals Semantic Analysis of Cultural Heritage News Propagation in Social Media: Assessing the Role of Media and Journalists in the Era of Big Data

2021 ◽  
Vol 13 (1) ◽  
pp. 341
Author(s):  
Theodora A. Maniou
Keyword(s):  
Social Media ◽  
Big Data ◽  
Cultural Heritage ◽  
Semantic Analysis ◽  
Audience Engagement ◽  
Media Environment ◽  
The Social ◽  
Social Media Platforms ◽  
Conflicting Role

In the era of big data, within the intense environment of social media, the effective communication of cultural heritage initiatives is considered of equal or—in some cases—even greater importance than heritage data themselves. Media and journalists play a critical and in some cases conflicting role in audience engagement and the sustainable promotion of cultural heritage narratives within the social media environment. The aim of this study was to assess the role of media and journalists in propagating cultural heritage news through social media platforms, and the narratives they tend to create in the digital public sphere. A qualitative approach is employed as a means of examining in-depth specific narratives, their meaning(s) and connotation(s), using semantic analysis.

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 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
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