Social Media Intelligence and Learning Environment: an Open Source Framework for Social Media Data Collection, Analysis and Curation

2019 ◽  
Author(s):  
Chen Wang ◽  
Luigi Marini ◽  
Chieh-Li Chin ◽  
Nickolas Vance ◽  
Curtis Donelson ◽  
...  
Keyword(s):  
Social Media ◽  
Data Collection ◽  
Learning Environment ◽  
Open Source ◽  
Social Media Data ◽  
Open Source Framework ◽  
Media Data ◽  
Social Media Intelligence

Social Media-Based Data Collection and Analysis in Educational Research

2020 ◽  
pp. 54-77
Author(s):  
Liuli Huang
Keyword(s):  
Social Media ◽  
Data Collection ◽  
Educational Research ◽  
Media In Education ◽  
Social Media Data ◽  
Educational Processes ◽  
Data Collection And Analysis ◽  
Media Data ◽  
Social Media In Education

The past decades have brought many changes to education, including the role of social media in education. Social media data offer educational researchers first-hand insights into educational processes. This is different from most traditional and often obtrusive data collection methods (e.g., interviews and surveys). Many researchers have explored the role of social media in education, such as the value of social media in the classroom, the relationship between academic achievement and social media. However, the role of social media in educational research, including data collection and analysis from social media, has been examined to a far lesser degree. This study seeks to discuss the potential of social media for educational research. The purpose of this chapter is to illustrate the process of collecting and analyzing social media data through a pilot study of current math educational conditions.

Sentiment Analysis of Tweets for Estimating Criticality and Security of Events

2020 ◽  
pp. 293-319 ◽  
Author(s):  
V. Subramaniyaswamy ◽  
R. Logesh ◽  
M. Abejith ◽  
Sunil Umasankar ◽  
A. Umamakeswari
Keyword(s):  
Social Media ◽  
Data Collection ◽  
Sentiment Analysis ◽  
Quantitative Result ◽  
Law Enforcement Agencies ◽  
Social Media Data ◽  
Software Application ◽  
The World ◽  
Media Data ◽  
Different Levels

Social Media has become one of the major industries in the world. It has been noted that almost three fourth of the world's population use social media. This has instigated many researches towards social media. One such useful application is the sentimental analysis of real time social media data for security purposes. The insights that are generated can be used by law enforcement agencies and for intelligence purposes. There are many types of analyses that have been done for security purposes. Here, the authors propose a comprehensive software application which will meticulously scrape data from Twitter and analyse them using the lexicon based analysis to look for possible threats. They propose a methodology to obtain a quantitative result called criticality to assess the level of threat for a public event. The results can be used to understand people's opinions and comments with regard to specific events. The proposed system combines this lexicon based sentimental analysis along with deep data collection and segregates the emotions into different levels to analyse the threat for an event.

Social Media Data Collection and Quality for Urban Studies

2021 ◽  
pp. 229-248
Author(s):  
Álvaro Bernabeu-Bautista ◽  
Leticia Serrano-Estrada ◽  
Pablo Martí
Keyword(s):  
Social Media ◽  
Data Collection ◽  
Urban Studies ◽  
Social Media Data ◽  
Media Data

scholarly journals Twitter Analyzer—How to Use Semantic Analysis to Retrieve an Atmospheric Image around Political Topics in Twitter

2019 ◽  
Vol 3 (3) ◽  
pp. 38 ◽  
Author(s):  
Stefan Spettel ◽  
Dimitrios Vagianos
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Open Source ◽  
Semantic Analysis ◽  
Time Window ◽  
Data Set ◽  
Social Media Data ◽  
Open Source Tool ◽  
Implementation Model ◽  
Media Data

Social media are heavily used to shape political discussions. Thus, it is valuable for corporations and political parties to be able to analyze the content of those discussions. This is exemplified by the work of Cambridge Analytica, in support of the 2016 presidential campaign of Donald Trump. One of the most straightforward metrics is the sentiment of a message, whether it is considered as positive or negative. There are many commercial and/or closed-source tools available which make it possible to analyze social media data, including sentiment analysis (SA). However, to our knowledge, not many publicly available tools have been developed that allow for analyzing social media data and help researchers around the world to enter this quickly expanding field of study. In this paper, we provide a thorough description of implementing a tool that can be used for performing sentiment analysis on tweets. In an effort to underline the necessity for open tools and additional monitoring on the Twittersphere, we propose an implementation model based exclusively on publicly available open-source software. The resulting tool is capable of downloading Tweets in real-time based on hashtags or account names and stores the sentiment for replies to specific tweets. It is therefore capable of measuring the average reaction to one tweet by a person or a hashtag, which can be represented with graphs. Finally, we tested our open-source tool within a case study based on a data set of Twitter accounts and hashtags referring to the Syrian war, covering a short time window of one week in the spring of 2018. The results show that while high accuracy of commercial or other complicated tools may not be achieved, our proposed open source tool makes it possible to get a good overview of the overall replies to specific tweets, as well as a practical perception of tweets, related to specific hashtags, identifying them as positive or negative.

Extended Implementation Method for Virtual Sensors: Web-Based Real-Time Transportation Data Collection and Analysis for Incident Management

2015 ◽  
Vol 2528 (1) ◽  
pp. 27-37 ◽  
Author(s):  
Abdullah Kurkcu ◽  
Ender Faruk Morgul ◽  
Kaan Ozbay
Keyword(s):  
Social Media ◽  
Data Collection ◽  
Travel Time ◽  
Real Time ◽  
Open Data ◽  
Data Sources ◽  
Incident Management ◽  
Virtual Sensors ◽  
Social Media Data ◽  
Media Data

Open data sources and social media data are gaining increasing attention as important information providers in transportation and incident management. In this paper, practical evidence for the emerging potential of online and open data sources is presented. The authors’ previous research on virtual sensors is combined and extended by integrating real-time incident information and social media network engagement. The fundamental contribution of this paper is the development of an extended virtual sensor framework to provide an automated travel time data collection method as incidents occur. In addition, social media data can be useful for more effective real-time incident response. The proposed framework can easily be modified and used to evaluate travel time effects of incidents on roadways and clearance times and to make use of social media data in obtaining time-critical incident-related information.

Conducting Semantic-Based Network Analyses from Social Media Data

2015 ◽  
pp. 369-427
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Network Analysis ◽  
Open Source ◽  
Social Media Data ◽  
Network Analyses ◽  
Social Media Platforms ◽  
Free Information ◽  
Keyword Searches ◽  
Media Data

Network analysis is widely used to mine social media. This involves both the study of structural metadata (information about information) and the related contents (the textual messaging, the related imagery, videos, URLs, and others). A semantic-based network analysis relies on the analysis of relationships between words and phrases (as meaningful concepts), and this approach may be applied effectively to social media data to extract insights. To gain a sense of how this might work, a trending topic of the day was chosen (namely, the free-information and data leakage movement) to see what might be illuminated using this semantic-based network analysis, an open-source technology, NodeXL, and access to multiple social media platforms. Three types of networks are extracted: (1) conversations (#hashtag microblogging networks on Twitter; #eventgraphs on Twitter; and keyword searches on Twitter; (2) contents (video networks on YouTube, related tags networks on Flickr, and article networks on Wikipedia; and (3) user accounts on Twitter, YouTube, Flickr, and Wikipedia.

scholarly journals Dynamics of Sexual Harassment on Social Media

2019 ◽  
Author(s):  
Faqihul Muqoddam ◽  
Virgin Suciyanti Maghfiroh
Keyword(s):  
Social Media ◽  
Data Collection ◽  
Sexual Harassment ◽  
Qualitative Method ◽  
Social Media Data ◽  
Data Collection Methods ◽  
Narrative Tradition ◽  
Almost All ◽  
Media Data ◽  
Collection Methods

Sexual harassment is a currently netizen’s habit on social media. Almost all of their comments on social media contain words to abuse. This study was conducted with the aim of analyzing the forms of sexual harassment and identifying the factors of sexual harassment on social media. This study uses a qualitative method of narrative tradition with a focus on investigating sexual harassment that occurs on social media. Data collection methods are carried out by observing comments of netizens. The characteristics of the comments that chosen in this study are those written on Instagram and point to the element of sexual harassment. The results show that sexual harassment on social media is happen with; 1. directly (explicitly), 2. indirectly (implicitly) according to the meaning of the sentence. Then, the factors of sexual harassment on social media are; 1. netizens are looking for attention (as evidenced by accounts that are used only fake accounts), 2. photo content or account owner captions that lead netizens to harass. Suggestions based on this study are the need to develop psychoeducation for adolescents and families both as subjects and victims so as to avoid sexual harassment behavior.

scholarly journals BESOCIAL: A Sustainable Knowledge Graph-Based Workflow for Social Media Archiving

2021 ◽  
Author(s):  
Sven Lieber ◽  
Dylan Van Assche ◽  
Sally Chambers ◽  
Fien Messens ◽  
Friedel Geeraert ◽  
...  
Keyword(s):  
Social Media ◽  
Open Source ◽  
Public Discourse ◽  
Heterogeneous Data ◽  
Knowledge Graph ◽  
Social Media Data ◽  
Heterogeneous Data Sources ◽  
Metadata Standards ◽  
Data Stewardship ◽  
Media Data

Social media as infrastructure for public discourse provide valuable information that needs to be preserved. Several tools for social media harvesting exist, but still only fragmented workflows may be formed with different combinations of such tools. On top of that, social media data but also preservation-related metadata standards are heterogeneous, resulting in a costly manual process. In the framework of BESOCIAL at the Royal Library of Belgium (KBR), we develop a sustainable social media archiving workflow that integrates heterogeneous data sources in a Europeana and PREMIS-based data model to describe data preserved by open source tools. This allows data stewardship on a uniform representation and we generate metadata records automatically via queries. In this paper, we present a comparison of social media harvesting tools and our Knowledge Graph-based solution which reuses off-the-shelf open source tools to harvest social media and automatically generate preservation-related metadata records. We validate our solution by generating Encoded Archival Description (EAD) and bibliographic MARC records for preservation of harvested social media collections from Twitter collected at KBR. Other archiving institutions can build upon our solution and customize it to their own social media archiving policies.

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