scholarly journals SEARCHING FOR THE FORGOTTEN: EXAMINING THE ONLINE PRESENCE OF VICTIMS OF POLICE KILLINGS

2021 ◽  
Author(s):  
Jennifer Pierre ◽  
Morgan Currie ◽  
Britt Paris ◽  
Irene Pasquetto
Keyword(s):  
Social Media ◽  
Potential Role ◽  
Media Content ◽  
Social Media Data ◽  
Social Media Mining ◽  
Source Of Information ◽  
Media Data ◽  
Official Police ◽  
Media Mining

This paper examines the potential role of social media in enhancing the understanding and perception of victims of police killings and the data collection surrounding these incidents. Through a series of content analysis and social media mining exercises, the authors observe the emergence of three distinct types of social media content offered on victims of police killings: persistence of the deceased’s activity across social media, sensational commentary on videos and blog postings, and memorials on Facebook, Twitter, and Tumblr. As part of a larger investigation of the availability and accessibility of official police homicide data, this paper aims to present social media data as a potentially powerful source of information to supplement quantitative reports. This process may be especially useful for the most affected communities, particularly BIPOC communities.

Mining on Social Media

2017 ◽  
pp. 206-222 ◽  
Author(s):  
Ambati Venkata Krishna Prasad ◽  
Venkata Naresh Mandhala
Keyword(s):  
Social Media ◽  
Social Network ◽  
Sentiment Analysis ◽  
Learning Method ◽  
Bayes Classifier ◽  
Social Media Data ◽  
Social Media Mining ◽  
Analysis Technique ◽  
Media Data ◽  
Media Mining

Social media mining is the process of representing, analyzing, and extracting actionable patterns and trends from raw social media data. Social media is favored by many users since it is available to individuals without any limitations to share their opinions, educational learning experiences and concerns via their status. Twitter API, twitter4j, is processed for searching the tweets based on the geo location. Student's posts on social network offers us a stronger concern to take decisions concerning the particular education system's learning method of the system. Evaluating knowledge in social media is sort of a difficult method. Bayes classifier are enforced on deep-mined knowledge for analysis purpose to urge the deeper understanding of the information. It uses multi label classification technique as every label falls into completely different classes. Label based measures are mostly taken to research the results and comparing them with the prevailing sentiment analysis technique.

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.

A Sentiment Analysis and Role of Twitter for Health Communications

2022 ◽  
pp. 188-205
Author(s):  
Erkan Çiçek ◽  
Uğur Gündüz
Keyword(s):  
Social Media ◽  
Crisis Management ◽  
Semantic Analysis ◽  
Social Media Data ◽  
Global Pandemic ◽  
The Difference ◽  
Textual Content ◽  
The Impact ◽  
Media Data

Social media has been in our lives so much lately that it is an undeniable fact that global pandemics, which constitute an important part of our lives, are also affected by these networks and that they exist in these networks and share the users. The purpose of making this hashtag analysis is to reveal the difference in discourse and language while analyzing Twitter data and to evaluate the effects of a global pandemic crisis on language, message, and crisis management with social media data. This form of analysis is typically completed through amassing textual content data then investigating the “sentiment” conveyed. Within the scope of the study, 11,300 Twitter messages posted with the #stayhome hashtag between 30 May 2020 and 6 June 2020 were examined. The impact and reliability of social media in disaster management could be questioned by carrying out a content analysis based totally on the semantic analysis of the messages given on the Twitter posts with the phrases and frequencies used.

scholarly journals Social Media Text Mining Framework for Drug Abuse: Development and Validation Study With an Opioid Crisis Case Analysis

10.2196/18350 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e18350 ◽  
Author(s):  
Tareq Nasralah ◽  
Omar El-Gayar ◽  
Yong Wang
Keyword(s):  
Social Media ◽  
Drug Abuse ◽  
Data Quality ◽  
Prescription Drugs ◽  
Case Analysis ◽  
Media Content ◽  
Opioid Epidemic ◽  
Social Media Data ◽  
Social Media Text ◽  
Media Data

Background Social media are considered promising and viable sources of data for gaining insights into various disease conditions and patients’ attitudes, behaviors, and medications. They can be used to recognize communication and behavioral themes of problematic use of prescription drugs. However, mining and analyzing social media data have challenges and limitations related to topic deduction and data quality. As a result, we need a structured approach to analyze social media content related to drug abuse in a manner that can mitigate the challenges and limitations surrounding the use of such data. Objective This study aimed to develop and evaluate a framework for mining and analyzing social media content related to drug abuse. The framework is designed to mitigate challenges and limitations related to topic deduction and data quality in social media data analytics for drug abuse. Methods The proposed framework started with defining different terms related to the keywords, categories, and characteristics of the topic of interest. We then used the Crimson Hexagon platform to collect data based on a search query informed by a drug abuse ontology developed using the identified terms. We subsequently preprocessed the data and examined the quality using an evaluation matrix. Finally, a suitable data analysis approach could be used to analyze the collected data. Results The framework was evaluated using the opioid epidemic as a drug abuse case analysis. We demonstrated the applicability of the proposed framework to identify public concerns toward the opioid epidemic and the most discussed topics on social media related to opioids. The results from the case analysis showed that the framework could improve the discovery and identification of topics in social media domains characterized by a plethora of highly diverse terms and lack of a commonly available dictionary or language by the community, such as in the case of opioid and drug abuse. Conclusions The proposed framework addressed the challenges related to topic detection and data quality. We demonstrated the applicability of the proposed framework to identify the common concerns toward the opioid epidemic and the most discussed topics on social media related to opioids.

scholarly journals Success in an Online Giving Day: The Role of Social Media in Fundraising

2019 ◽  
Vol 49 (1) ◽  
pp. 74-92 ◽  
Author(s):  
Abhishek Bhati ◽  
Diarmuid McDonnell
Keyword(s):  
Social Media ◽  
Organizational Factors ◽  
Network Size ◽  
Audience Engagement ◽  
Social Media Data ◽  
Size Number ◽  
Social Media Platforms ◽  
The Relationship ◽  
Media Data

Social media platforms offer nonprofits considerable potential for crafting, supporting, and executing successful fundraising campaigns. How impactful are attempts by these organizations to utilize social media to support fundraising activities associated with online Giving Days? We address this question by testing a number of hypotheses of the effectiveness of using Facebook for fundraising purposes by all 704 nonprofits participating in Omaha Gives 2015. Using linked administrative and social media data, we find that fundraising success—as measured by the number of donors and value of donations—is positively associated with a nonprofit’s Facebook network size (number of likes), activity (number of posts), and audience engagement (number of shares), as well as net effects of organizational factors including budget size, age, and program service area. These results provide important new empirical insights into the relationship between social media utilization and fundraising success of nonprofits.

Information Visualizations as a Tool to Study Users’ Social Media Privacy Concerns

2018 ◽  
Author(s):  
Anatoliy Gruzd ◽  
Jenna Jacobson ◽  
Elizabeth Dubois
Keyword(s):  
Social Media ◽  
Online Survey ◽  
Job Applicants ◽  
Social Media Data ◽  
Privacy Concerns ◽  
Comfort Levels ◽  
People’S Attitudes ◽  
Media Data ◽  
Information Visualizations

The amount and complexity of data that can be accessed from social media has been increasing exponentially. We examine the value of using information visualizations as a tool to study people’s attitudes and perceptions regarding their social media data being used by third parties. In the context of using social media to screen job applicants, we investigate the role of visualizations in studying users’ social media privacy concerns. Utilizing an online survey of 454 participants, we compare participants’ comfort levels in relation to different types of publicly accessible social media data. The results partially support the supposition that analytical information based on some form of data analysis will receive a stronger reaction when accompanied by representative visualizations.

Probabilistic Models for Social Media Mining

2014 ◽  
pp. 95-105
Author(s):  
Flora S. Tsai
Keyword(s):  
Social Media ◽  
Real World ◽  
Probabilistic Models ◽  
The Other ◽  
Media Content ◽  
Real World Data ◽  
Social Media Mining ◽  
Additional Information ◽  
Classification Framework ◽  
Media Mining

This paper proposes probabilistic models for social media mining based on the multiple attributes of social media content, bloggers, and links. The authors present a unique social media classification framework that computes the normalized document-topic matrix. After comparing the results for social media classification on real-world data, the authors find that the model outperforms the other techniques in terms of overall precision and recall. The results demonstrate that additional information contained in social media attributes can improve classification and retrieval results.

Harnessing social media to understand tourist mobility: the role of information technology and big data

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jinyan Chen ◽  
Susanne Becken ◽  
Bela Stantic
Keyword(s):  
Social Media ◽  
Literature Review ◽  
Current Trend ◽  
Social Media Data ◽  
Content Type ◽  
Use Of Data ◽  
Social Media Platforms ◽  
The City ◽  
Media Data

Purpose This paper aims to examine key parameters of scholarly context and geographic focus and provide an assessment of theoretical underpinnings of studies in the field of social media and visitor mobility. This review also summarised the characteristics of social media data, including how data are collected from different social media platforms and their advantages and limitations. The stocktake of research in this field was completed by examining technologies and applied methods that supported different research questions. Design/methodology/approach This literature review applied a mix of methods to conduct a literature review. This review analysed 82 journal articles on using social media to track visitors’ movements between 2014 and November 2020. The literature compared the different social media, discussed current applied theories, available technologies, analysed the current trend and provided advice for future directions. Findings This review provides a state-of-the-art assessment of the research to date on tourist mobility analysed using social media data. The diversity of scales (with a dominant focus on the city-scale), platforms and methods highlight that this field is emerging, but it also reflects the complexity of the tourism phenomenon. This review identified a lack of theory in this field, and it points to ongoing challenges in ensuring appropriate use of data (e.g. differentiating travellers from residents) and the ethics surrounding them. Originality/value The findings guide researchers, especially those with no computer science background, on the different types of approaches, data sources and methods available for tracking tourist mobility by harnessing social media. Depending on the particular research interest, different tools for processing and visualization are available.

scholarly journals The role of social media data in operations and production management

2015 ◽  
Vol 55 (17) ◽  
pp. 5027-5036 ◽  
Author(s):  
Hing Kai Chan ◽  
Ewelina Lacka ◽  
Rachel W.Y. Yee ◽  
Ming K. Lim
Keyword(s):  
Social Media ◽  
Production Management ◽  
Social Media Data ◽  
Media Data
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