scholarly journals RESEARCHING ONLINE LABOR STRIKE AND PROTEST PREDICTION TECHNOLOGIES

2020 ◽  
Author(s):  
Gabriel Grill
Keyword(s):  
Social Media ◽  
Data Science ◽  
Science Research ◽  
Research Field ◽  
Civil Unrest ◽  
Academic Knowledge ◽  
Computer Science Research ◽  
Social Media Platforms ◽  
Online Labor ◽  
Military Law

Efforts to surveil social media platforms at scale using big data techniques have recently manifested in government-funded research to predict protests following the election of President Trump. This work is part of a computer science research field focused on online “civil unrest prediction” dedicated to forecasting protests across the globe (e.g. Indonesia, Brazil and Australia). Researchers draw upon established data science techniques such as event detection/prediction, but also specific approaches for surveilling social movements are conceived. Besides furthering the academic knowledge-base on civil unrest and protests, the works in this field envision to support a variety of stakeholders with different interests such as governments, the military, law enforcement, human rights organizations and industries such as insurance and supply chain management. I analyze the recent history of civil unrest prediction on social media platforms through examining discourses, implicated actors and technological affordances as encountered in publications and other public online artifacts. In this paper I discuss different risk frames employed by researchers, concerning politics of the technology and argue for a needed public debate on the role of online protest surveillance in democratic societies.

scholarly journals The R package “eseis” – a software toolbox for environmental seismology

2018 ◽  
Vol 6 (3) ◽  
pp. 669-686 ◽  
Author(s):  
Michael Dietze
Keyword(s):  
Data Science ◽  
Earth Science ◽  
Science Research ◽  
Worked Examples ◽  
R Package ◽  
Research Field ◽  
Data Sets ◽  
Research Fields ◽  
Scientific Disciplines ◽  
Analysis Environment

Abstract. Environmental seismology is the study of the seismic signals emitted by Earth surface processes. This emerging research field is at the intersection of seismology, geomorphology, hydrology, meteorology, and further Earth science disciplines. It amalgamates a wide variety of methods from across these disciplines and ultimately fuses them in a common analysis environment. This overarching scope of environmental seismology requires a coherent yet integrative software which is accepted by many of the involved scientific disciplines. The statistic software R has gained paramount importance in the majority of data science research fields. R has well-justified advances over other mostly commercial software, which makes it the ideal language to base a comprehensive analysis toolbox on. The article introduces the avenues and needs of environmental seismology, and how these are met by the R package eseis. The conceptual structure, example data sets, and available functions are demonstrated. Worked examples illustrate possible applications of the package and in-depth descriptions of the flexible use of the functions. The package has a registered DOI, is available under the GPL licence on the Comprehensive R Archive Network (CRAN), and is maintained on GitHub.

Use of Social Media for Knowledge Sharing by Instructors in a Higher Education Institution

2018 ◽  
pp. 116-143
Author(s):  
Stephen Asunka
Keyword(s):  
Social Media ◽  
Knowledge Sharing ◽  
Education Institution ◽  
Future Research ◽  
Online Questionnaire ◽  
Group Discussions ◽  
Academic Knowledge ◽  
Social Media Platforms ◽  
Applicable Knowledge ◽  
Use Of Social Media

Against the backdrop that universities are required to generate and disseminate relevant and applicable knowledge for the general good, and with the understanding that social media can be an effective vehicle for such knowledge sharing practices, this study explored the use of social media for knowledge sharing by academics at a university college in Ghana. The study thus examined how instructors use social media for sharing academic knowledge, the factors that promote such knowledge sharing practices, and the barriers to effective knowledge sharing in the academic environment. 47 instructors participated by completing an online questionnaire, whilst 7 participated in focus group discussions. Findings reveal a regular, though not daily, use of social media platforms for academic knowledge sharing. Personal, technological and institutional factors were determined to be contributing in fostering as well as hindering such activities. Implications of these findings as well as suggestions for future research are accordingly discussed.

A Historical Review of Immersive Storytelling Technologies

2021 ◽  
pp. 569-597
Author(s):  
Hector Puente Bienvenido ◽  
Borja Barinaga ◽  
Jorge Mora-Fernandez
Keyword(s):  
Social Media ◽  
Video Games ◽  
User Experience ◽  
Data Science ◽  
Interactive Media ◽  
Historical Review ◽  
Research Field ◽  
Media Arts ◽  
Interactive Storytelling ◽  
History Of

This chapter is focused on describing the history and the current relevance of user experience (UX) techniques that combine data science and AI in the research field of interactive and immersive storytelling, including virtual and augmented realities. It initially presents a brief history of interactive storytelling, video games, VR and AR, AI and data science, and the user experience (UX) techniques used in those areas. Later, the chapter describes the UX techniques in depth, using AI and data science that work best and are more useful for testing interactive media products, describing examples of its applications briefly. Finally, the chapter presents conclusions in relationship with utopias and dystopias regarding the future use of UX, AI, and data science in several areas such as edutainment, social media, media arts, and business, among others.

scholarly journals A Microservice-Based Big Data Analysis Platform for Online Educational Applications

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Kehua Miao ◽  
Jie Li ◽  
Wenxing Hong ◽  
Mingtao Chen
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Science ◽  
Modular Design ◽  
Science Research ◽  
Big Data Analysis ◽  
Research Field ◽  
Traditional Work ◽  
Educational Applications ◽  
Analysis Platform

The booming development of data science and big data technology stacks has inspired continuous iterative updates of data science research or working methods. At present, the granularity of the labor division between data science and big data is more refined. Traditional work methods, from work infrastructure environment construction to data modelling and analysis of working methods, will greatly delay work and research efficiency. In this paper, we focus on the purpose of the current friendly collaboration of the data science team to build data science and big data analysis application platform based on microservices architecture for education or nonprofessional research field. In the environment based on microservices that facilitates updating the components of each component, the platform has a personal code experiment environment that integrates JupyterHub based on Spark and HDFS for multiuser use and a visualized modelling tools which follow the modular design of data science engineering based on Greenplum in-database analysis. The entire web service system is developed based on spring boot.

scholarly journals The R package eseis – a comprehensive software toolbox for environmental seismology

2018 ◽  
Author(s):  
Michael Dietze
Keyword(s):  
Data Science ◽  
Earth Science ◽  
Science Research ◽  
Worked Examples ◽  
R Package ◽  
Research Field ◽  
Data Sets ◽  
Research Fields ◽  
Scientific Disciplines ◽  
Analysis Environment

Abstract. Environmental seismology is the study of the seismic signals emitted by Earth surface processes. This emerging research field is at the seams of seismology, geomorphology, hydrology, meteorology, and further Earth science disciplines. It amalgamates a wide variety of methods from across these disciplines and, ultimately, fuses them in a common analysis environment. This overarching scope of environmental seismology asks for a coherent, yet integrative software, which is accepted by many of the involved scientific disciplines. The statistic software R has gained paramount importance in the majority of data science research fields. R has well justified advances over other, mostly commercial software, which makes it the ideal language to base a comprehensive analysis toolbox on. The article introduces the avenues and needs of environmental seismology, and how these are met by the R package eseis. The conceptual structure, example data sets and available functions are demonstrated. Worked examples illustrate possible applications of the package and in depth descriptions of the flexible use of the functions. The package is available under the GPL license on the Comprehensive R Archive Network (CRAN) and maintained on Github.

Introduction

2018 ◽  
Author(s):  
Samuel C. Woolley ◽  
Philip N. Howard
Keyword(s):  
Social Media ◽  
Political Communication ◽  
Data Science ◽  
Autonomous Agents ◽  
Future Research ◽  
Political Role ◽  
The People ◽  
Social Media Platforms ◽  
Qualitative Work

Computational propaganda is an emergent form of political manipulation that occurs over the Internet. The term describes the assemblage of social media platforms, autonomous agents, algorithms, and big data tasked with manipulating public opinion. Our research shows that this new mode of interrupting and influencing communication is on the rise around the globe. Advances in computing technology, especially around social automation, machine learning, and artificial intelligence, mean that computational propaganda is becoming more sophisticated and harder to track. This introduction explores the foundations of computational propaganda. It describes the key role of automated manipulation of algorithms in recent efforts to control political communication worldwide. We discuss the social data science of political communication and build upon the argument that algorithms and other computational tools now play an important political role in news consumption, issue awareness, and cultural understanding. We unpack key findings of the nine country case studies that follow—exploring the role of computational propaganda during events from local and national elections in Brazil to the ongoing security crisis between Ukraine and Russia. Our methodology in this work has been purposefully mixed, using quantitative analysis of data from several social media platforms and qualitative work that includes interviews with the people who design and deploy political bots and disinformation campaigns. Finally, we highlight original evidence about how this manipulation and amplification of disinformation is produced, managed, and circulated by political operatives and governments, and describe paths for both democratic intervention and future research in this space.

scholarly journals Social Media during the Egyptian Revolution: Egyptian Nationals at Home and Abroad

2013 ◽  
Vol 6 (3) ◽  
Author(s):  
Margot Buchanan ◽  
Soha El Batrawy
Keyword(s):  
Social Media ◽  
Digital Media ◽  
Small Groups ◽  
Video Content ◽  
Egyptian Revolution ◽  
Civil Unrest ◽  
Shared Content ◽  
Social Media Platforms ◽  
The Uk ◽  
At Home

This article considers the significance of social media platforms during the 2011 Egyptian Revolution to two small groups of Egyptian nationals. Interviews were conducted with small groups of Egyptians living in the UK and Egyptians living at home. It establishes how these citizens used social media during the revolution and whether during the days of civil unrest they became citizen journalists by accessing and sharing information and video content with family and friends via digital media platforms such as Facebook and YouTube. This research found that the sharing of online revolutionary content was dependent upon the level of trust with which the interviewees regarded its source. Significantly, interviewees in the UK were reluctant to share any content they received through social media platforms, and trusted only sources that they judged were ‘reliable’, while interviewees in Egypt shared content that was posted by fellow citizens regardless of whether or not they completely trusted the source.

scholarly journals Potential Biases in Big Data: Omitted Voices on Social Media

2018 ◽  
Vol 38 (1) ◽  
pp. 10-24 ◽  
Author(s):  
Eszter Hargittai
Keyword(s):  
Social Media ◽  
Big Data ◽  
Social Science Research ◽  
Science Research ◽  
National Sample ◽  
Sociodemographic Factors ◽  
User Characteristics ◽  
Business Interests ◽  
Internet Skills ◽  
Social Media Platforms

While big data offer exciting opportunities to address questions about social behavior, studies must not abandon traditionally important considerations of social science research such as data representativeness and sampling biases. Many big data studies rely on traces of people’s behavior on social media platforms such as opinions expressed through Twitter posts. How representative are such data? Whose voices are most likely to show up on such sites? Analyzing survey data about a national sample of American adults’ social network site usage, this article examines what user characteristics are associated with the adoption of such sites. Findings suggest that several sociodemographic factors relate to who adopts such sites. Those of higher socioeconomic status are more likely to be on several platforms suggesting that big data derived from social media tend to oversample the views of more privileged people. Additionally, Internet skills are related to using such sites, again showing that opinions visible on these sites do not represent all types of people equally. The article cautions against relying on content from such sites as the sole basis of data to avoid disproportionately ignoring the perspectives of the less privileged. Whether business interests or policy considerations, it is important that decisions that concern the whole population are not based on the results of analyses that favor the opinions of those who are already better off.

scholarly journals Tweet Retrieval and Analysing the Trends

2021 ◽  
Vol 10 (2) ◽  
pp. 34-38
Author(s):  
Utkarsh Malik ◽  
◽  
Harpreet Kaur ◽  
Aditi Chaudhary ◽  
◽  
...  
Keyword(s):  
Social Media ◽  
Data Science ◽  
Text Processing ◽  
Political Polarization ◽  
Product Analysis ◽  
Protest Movements ◽  
Social Media Platforms ◽  
Media Platform ◽  
Processing Techniques ◽  
Share Information

We can’t disregard the importance of Social Media in Today’s Technology Era. Internet is almost in every hand. People uses various Social Media platforms to express themselves and their thinking about various topics such as Politics, Entertainment, Sports, etc. In the Data Science industry, trend analysis can be used for several purposes like marketing or product analysis. Twitter data has been used to analyze political polarization and the spread of protest movements. Twitter is one of the most popular social media platform that allows the users to spread and share information. Twitter publishes the list of recent or latest topics named as “Trending Topics” which shows all the happenings in the world and what are the people’s opinions about those topics. This Trend Analyzer will work on a given set of tweets and generates a graph based on the tweets and showsthe comparative popularity of the used hashtags. This Analyzer will examine a set of tweets using Python and text-processing techniques

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