Inducing Six-Word Stories From Curated Text Sets to Anticipate Cyberwar in 4IR

2018 ◽  
pp. 406-477
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Industrial Revolution ◽  
Data Sources ◽  
Textual Information ◽  
Fourth Industrial Revolution ◽  
Multiple Dimensions ◽  
Induction Process ◽  
Research Questions ◽  
Time Periods ◽  
Frequency Counts

From curated “cyberwar” text sets (from government, mainstream journalism, academia, and social media), six-word stories are computationally induced (using word frequency counts, text searches, word network analysis, word clustering, and other means), supported by post-induction human writing. The resulting inducted six-word stories are used to (1) describe and summarize the underlying textual information (to enable a bridge to a complex topic); (2) produce insights about the underlying textual information and related in-world phenomena; and (3) answer particular research questions. These resulting six-word stories are analyzed along multiple dimensions: data sources (government, journalism, academia, and social media), expert calls-and-crowd responses, and by time periods (pre-cyberwar and cyberwar periods). The efficacy of this six-word story induction process is evaluated, and the extracted six-word stories are applied to cyberwar potentials during the Fourth Industrial Revolution (4IR).

Safe Distances

2019 ◽  
pp. 304-340
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Industrial Revolution ◽  
Research Literature ◽  
Abductive Reasoning ◽  
Personal Relationships ◽  
The Real ◽  
Fourth Industrial Revolution

Online human-to-human (and human-to-robot) hyper-personal relationships have evolved over the years, and their prevalence has broadened the available cyberattack surfaces. With the deployment of malicious socialbots on social media in the virtual and AI-informed embodied socialbots in the real, human interests in socializing have become more fraught and risky. Based on the research literature, abductive reasoning from in-world experiences, and analogical analysis to project into the Fourth Industrial Revolution, this work suggests the importance of greater awareness of the risks in interrelating in the virtual and the real and suggests that there are no safe distances.

scholarly journals COMMUNICATING A TRUSTWORTHY ONLINE ORGANISATIONAL IDENTITY WITH CHATBOTS

2019 ◽  
Vol 2019 ◽  
Author(s):  
Indra Ayu Susan Mckie ◽  
Bhuva Narayan
Keyword(s):  
Social Media ◽  
User Experience ◽  
Search Engines ◽  
Industrial Revolution ◽  
Symbolic Representation ◽  
The Internet ◽  
Web Browsers ◽  
Online Identity ◽  
Fourth Industrial Revolution ◽  
Organisational Identity

Conversational bots, otherwise known as chatbots, operate within the fourth industrial revolution as a client facing form of AI. They are communicative interfaces that mimic human conversation to deliver information in a highly personalised way. The user experience of chatbots can change the way individuals, groups and organisations define themselves online (Whitley, Gal & Kjaergaard, 2014). This paper discusses the opportunities in building an online identity via chatbots, with emphasis on harnessing the properties of chatbots to develop trust with users. Currently, organisations are limited to the properties and affordances of web browsers, search engines and social media to communicate a “shared symbolic representation” (Gioia, 1998). This paper focuses on organisational identities on the Internet, and details both opportunities and vulnerabilities in establishing trust with users through chatbots.

A systematic review of text mining approaches applied to various application areas in the biomedical domain

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sudha Cheerkoot-Jalim ◽  
Kavi Kumar Khedo
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Biomedical Application ◽  
Data Sources ◽  
Biomedical Text ◽  
Biomedical Text Mining ◽  
Biomedical Domain ◽  
Content Type ◽  
Health Related ◽  
Research Questions

Purpose This work shows the results of a systematic literature review on biomedical text mining. The purpose of this study is to identify the different text mining approaches used in different application areas of the biomedical domain, the common tools used and the challenges of biomedical text mining as compared to generic text mining algorithms. This study will be of value to biomedical researchers by allowing them to correlate text mining approaches to specific biomedical application areas. Implications for future research are also discussed. Design/methodology/approach The review was conducted following the principles of the Kitchenham method. A number of research questions were first formulated, followed by the definition of the search strategy. The papers were then selected based on a list of assessment criteria. Each of the papers were analyzed and information relevant to the research questions were extracted. Findings It was found that researchers have mostly harnessed data sources such as electronic health records, biomedical literature, social media and health-related forums. The most common text mining technique was natural language processing using tools such as MetaMap and Unstructured Information Management Architecture, alongside the use of medical terminologies such as Unified Medical Language System. The main application area was the detection of adverse drug events. Challenges identified included the need to deal with huge amounts of text, the heterogeneity of the different data sources, the duality of meaning of words in biomedical text and the amount of noise introduced mainly from social media and health-related forums. Originality/value To the best of the authors’ knowledge, other reviews in this area have focused on either specific techniques, specific application areas or specific data sources. The results of this review will help researchers to correlate most relevant and recent advances in text mining approaches to specific biomedical application areas by providing an up-to-date and holistic view of work done in this research area. The use of emerging text mining techniques has great potential to spur the development of innovative applications, thus considerably impacting on the advancement of biomedical research.

A Content Analysis of Nine Literacy Journals, 2009-2014

2016 ◽  
Vol 48 (4) ◽  
pp. 476-502 ◽  
Author(s):  
Seth A. Parsons ◽  
Melissa A. Gallagher ◽  
Keyword(s):  
Content Analysis ◽  
Teaching And Learning ◽  
Data Sources ◽  
Chi Square ◽  
Theoretical Perspectives ◽  
Rigorous Research ◽  
Research Designs ◽  
Research Questions ◽  
Frequency Counts ◽  
Literacy Research

The purpose of this study was to determine the topics being studied, theoretical perspectives being used, and methods being implemented in current literacy research. A research team completed a content analysis of nine journals from 2009 to 2014 to gather data. In the 1,238 articles analyzed, the topics, theoretical perspectives, research designs, and data sources were recorded. Frequency counts of these findings are presented for each journal. Chi-square tests of independence revealed statistically significant differences among the topics, theoretical perspectives, designs, and data sources across the nine journals. These results suggest that the field of literacy research may be fragmented, which has been a concern for literacy researchers since the paradigm wars of the 1980s and 1990s. We urge the literacy research community to continue to demand rigorous research, but to do so in a way that appreciates the power in viewing and studying teaching and learning from diverse perspectives, using diverse methods, and with recognition that a foundational aspect of rigorous research is the match between research questions asked and research methods used.

scholarly journals Collective Intelligence: An Emerging World in Open Innovation

2019 ◽  
Vol 11 (16) ◽  
pp. 4495 ◽  
Author(s):  
JinHyo Joseph Yun ◽  
EuiSeob Jeong ◽  
Xiaofei Zhao ◽  
Sung Deuk Hahm ◽  
KyungHun Kim
Keyword(s):  
Pharmaceutical Industry ◽  
Automotive Industry ◽  
Open Innovation ◽  
Industrial Revolution ◽  
Collective Intelligence ◽  
Pharmaceutical Patents ◽  
Fourth Industrial Revolution ◽  
Research Questions ◽  
The Relationship ◽  
Inside Out

Responding to the lack of empirical research on the effect of collective intelligence on open innovation in the fourth industrial revolution, we examined the relationship between collective intelligence and open innovation. Collective intelligence or crowd innovation not only produces creative ideas or inventions, but also moderates any firm to innovate inside-out, outside-in, or in a coupled manner. We asked the following research questions: Does collective intelligence (or crowd innovation) motivate open innovation? Is there any difference in the effect of collective intelligence on open innovation by industry? These research questions led to the following three hypotheses: (1) Collective intelligence increases the performance of a firm, (2) collective intelligence will moderate the effect of open innovation, and (3) differences exist between the automotive industry and the pharmaceutical industry in these two effects. To empirically examine these three hypotheses, we analyzed the registered patents of these two industries from 2000 to 2014 over a 15-year period. These automotive and pharmaceutical patents were registered in the B60 category and the A61K category of the Korea Patent office, respectively. Collective intelligence was measured by co-invention. We found differences in the effects of collective intelligence on open innovation between the two industries. In the automotive industry, collective intelligence not only directly increased the performance, but also indirectly moderated the open innovation effect. However, this was not the case for the pharmaceutical industry.

scholarly journals Measuring the Impact of Financial News and Social Media on Stock Market Modeling Using Time Series Mining Techniques

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 181 ◽  
Author(s):  
Foteini Kollintza-Kyriakoulia ◽  
Manolis Maragoudakis ◽  
Anastasia Krithara
Keyword(s):  
Social Media ◽  
Time Series ◽  
Time Series Data ◽  
Technical Analysis ◽  
Series Data ◽  
Textual Information ◽  
Public Opinions ◽  
Time Periods ◽  
The Impact ◽  
Matching Techniques

In this work, we study the task of predicting the closing price of the following day of a stock, based on technical analysis, news articles and public opinions. The intuition of this study lies in the fact that technical analysis contains information about the event, but not the cause of the change, while data like news articles and public opinions may be interpreted as a cause. The paper uses time series analysis techniques such as Symbolic Aggregate Approximation (SAX) and Dynamic Time Warping (DTW) to study the existence of a relation between price data and textual information, either from news or social media. Pattern matching techniques from time series data are also incorporated, in order to experimentally validate potential correlations of price and textual information within given time periods. The ultimate goal is to create a forecasting model that exploits the previously discovered patterns in order to augment the forecasting accuracy. Results obtained from the experimental phase are promising. The performance of the classifier shows clear signs of improvement and robustness within the time periods where patterns between stock price and the textual information have been identified, compared to the periods where patterns did not exist.

Safe Distances

2018 ◽  
pp. 27-63
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Industrial Revolution ◽  
Research Literature ◽  
Abductive Reasoning ◽  
Personal Relationships ◽  
The Real ◽  
Fourth Industrial Revolution

Online human-to-human (and human-to-robot) hyper-personal relationships have evolved over the years, and their prevalence has broadened the available cyberattack surfaces. With the deployment of malicious socialbots on social media in the virtual and AI-informed embodied socialbots in the real, human interests in socializing have become more fraught and risky. Based on the research literature, abductive reasoning from in-world experiences, and analogical analysis to project into the Fourth Industrial Revolution, this work suggests the importance of greater awareness of the risks in interrelating in the virtual and the real and suggests that there are no safe distances.

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