scholarly journals Artificial Intelligent Technologies for the Construction Industry: How Are They Perceived and Utilized in Australia?

2022 ◽  
Vol 8 (1) ◽  
pp. 16
Author(s):  
Massimo Regona ◽  
Tan Yigitcanlar ◽  
Bo Xia ◽  
Rita Yi Man Li
Keyword(s):  
Social Media ◽  
Technology Adoption ◽  
Construction Industry ◽  
Methodological Approach ◽  
Public Perceptions ◽  
Social Media Analytics ◽  
Social Media Data ◽  
The Public ◽  
Negative Perceptions ◽  
Media Data

Artificial intelligence (AI) is a powerful technology that can be utilized throughout a construction project lifecycle. Transition to incorporate AI technologies in the construction industry has been delayed due to the lack of know-how and research. There is also a knowledge gap regarding how the public perceives AI technologies, their areas of application, prospects, and constraints in the construction industry. This study aims to explore AI technology adoption prospects and constraints in the Australian construction industry by analyzing social media data. This study adopted social media analytics, along with sentiment and content analyses of Twitter messages (n = 7906), as the methodological approach. The results revealed that: (a) robotics, internet-of-things, and machine learning are the most popular AI technologies in Australia; (b) Australian public sentiments toward AI are mostly positive, whilst some negative perceptions exist; (c) there are distinctive views on the opportunities and constraints of AI among the Australian states/territories; (d) timesaving, innovation, and digitalization are the most common AI prospects; and (e) project risk, security of data, and lack of capabilities are the most common AI constraints. This study is the first to explore AI technology adoption prospects and constraints in the Australian construction industry by analyzing social media data. The findings inform the construction industry on public perceptions and prospects and constraints of AI adoption. In addition, it advocates the search for finding the most efficient means to utilize AI technologies. The study helps public perceptions and prospects and constraints of AI adoption to be factored in construction industry technology adoption.

Social media analytics: how they support company public relations

2019 ◽  
Vol 40 (1) ◽  
pp. 28-34 ◽  
Author(s):  
Lisa Tam ◽  
Jeong-Nam Kim
Keyword(s):  
Social Media ◽  
Public Relations ◽  
Design Methodology ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Content Type ◽  
The Public ◽  
The Public Sphere ◽  
Use Of Social Media ◽  
Media Data

Purpose In the midst of practitioners’ increasing use of social media analytics (SMA) in guiding public relations (PR) strategy, this paper aims to present the capabilities and limitations of these tools and offers suggestions on how to best use them to gain research-based insights. Design/methodology/approach This review assesses the capabilities and limitations of SMA tools based on industry reports and research articles on trends in PR and SMA. Findings The strengths of SMA tools lie in their capability to gather and aggregate a large quantity of real-time social media data, use algorithms to analyze the data and present the results in ways meaningful to organizations and understand networks of issues and publics. However, there are also challenges, including the increasing restricted access to social media data, the increased use of bots, skewing social conversations in the public sphere, the lack of capability to analyze certain types of data, such as visual data and the discrepancy between data collected on social media and through other methods. Originality/value This review suggests that PR professionals acknowledge the capabilities and limitations of SMA tools when using them to inform strategy.

scholarly journals Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem

2021 ◽  
pp. 089443932110122
Author(s):  
Dennis Assenmacher ◽  
Derek Weber ◽  
Mike Preuss ◽  
André Calero Valdez ◽  
Alison Bradshaw ◽  
...  
Keyword(s):  
Social Media ◽  
Data Sharing ◽  
Algorithm Design ◽  
Computational Social Science ◽  
Evaluation Framework ◽  
Social Media Analytics ◽  
Data Sets ◽  
The Public ◽  
Research Areas ◽  
Media Data

Computational social science uses computational and statistical methods in order to evaluate social interaction. The public availability of data sets is thus a necessary precondition for reliable and replicable research. These data allow researchers to benchmark the computational methods they develop, test the generalizability of their findings, and build confidence in their results. When social media data are concerned, data sharing is often restricted for legal or privacy reasons, which makes the comparison of methods and the replicability of research results infeasible. Social media analytics research, consequently, faces an integrity crisis. How is it possible to create trust in computational or statistical analyses, when they cannot be validated by third parties? In this work, we explore this well-known, yet little discussed, problem for social media analytics. We investigate how this problem can be solved by looking at related computational research areas. Moreover, we propose and implement a prototype to address the problem in the form of a new evaluation framework that enables the comparison of algorithms without the need to exchange data directly, while maintaining flexibility for the algorithm design.

scholarly journals Social Media Analytics: Data Utilization of Social Media for Research

2019 ◽  
Vol 1 (2) ◽  
pp. 193-205
Author(s):  
Ria Andryani ◽  
Edi Surya Negara ◽  
Dendi Triadi
Keyword(s):  
Social Media ◽  
Public Perception ◽  
Data Retrieval ◽  
Media Analysis ◽  
Internet Technology ◽  
Mining Machine ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Visualization Techniques ◽  
Media Data

The amount of production data generated by social media opportunities that can be exploited by various parties, both government and private sectors to produce the information. Social media data can be used to know the behavior and public perception of the phenomenon or a particular event. To obtain and analyze social media data needed depth knowledge of Internet technology, social media, databases, data structures, information theory, data mining, machine learning, until the data and information visualization techniques. In this research, social media analysis on a particular topic and the development of prototype devices software used as a tool of social media data retrieval or retrieval of data applications. Social Media Analytics (SMA) aims to make the process of analysis and synthesis of social media data to produce information can be used by those in need. SMA process is done in three stages, namely: Capture, Understand and Present. This research is exploratorily focused on understanding the technology that became the basis of social media using various techniques exist and is already used in the study of social media analytic previously.

scholarly journals Artificial Intelligence-enabled analysis of social media data to understand public perceptions of COVID-19 contact tracing apps (Preprint)

10.2196/26618 ◽  
2020 ◽  
Author(s):  
Kathrin Cresswell ◽  
Ahsen Tahir ◽  
Zakariya Sheikh ◽  
Zain Hussain ◽  
Andrés Domínguez Hernández ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Public Perceptions ◽  
Contact Tracing ◽  
Social Media Data ◽  
Media Data

Destination image through social media analytics and survey method

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Michael S. Lin ◽  
Yun Liang ◽  
Joanne X. Xue ◽  
Bing Pan ◽  
Ashley Schroeder
Keyword(s):  
Social Media ◽  
Survey Data ◽  
Destination Image ◽  
Social Media Analytics ◽  
Tourism Destination ◽  
Social Media Data ◽  
Content Type ◽  
Holistic Understanding ◽  
Intercept Survey ◽  
Media Data

Purpose Recent tourism research has adopted social media analytics (SMA) to examine tourism destination image (TDI) and gain timely insights for marketing purposes. Comparing the methodologies of SMA and intercept surveys would provide a more in-depth understanding of both methodologies and a more holistic understanding of TDI than each method on their own. This study aims to investigate the unique merits and biases of SMA and a traditional visitor intercept survey. Design/methodology/approach This study collected and compared data for the same tourism destination from two sources: responses from a visitor intercept survey (n = 1,336) and Flickr social media photos and metadata (n = 11,775). Content analysis, machine learning and text analysis techniques were used to analyze and compare the destination image represented from both methods. Findings The results indicated that the survey data and social media data shared major similarities in the identified key image phrases. Social media data revealed more diverse and more specific aspects of the destination, whereas survey data provided more insights in specific local landmarks. Survey data also included additional subjective judgment and attachment towards the destination. Together, the data suggested that social media data should serve as an additional and complementary source of information to traditional survey data. Originality/value This study fills a research gap by comparing two methodologies in obtaining TDI: SMA and a traditional visitor intercept survey. Furthermore, within SMA, photo and metadata are compared to offer additional awareness of social media data’s underlying complexity. The results showed the limitations of text-based image questions in surveys. The findings provide meaningful insights for tourism marketers by having a more holistic understanding of TDI through multiple data sources.

Social Media to Social Media Analytics

2019 ◽  
Vol 10 (2) ◽  
pp. 57-70 ◽  
Author(s):  
Vikas Kumar ◽  
Pooja Nanda
Keyword(s):  
Social Media ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Ethical Concerns ◽  
Ethical Implications ◽  
The Social ◽  
Social Media Platforms ◽  
The Individual ◽  
The Impact ◽  
Media Data

With the amplification of social media platforms, the importance of social media analytics has exponentially increased for many brands and organizations across the world. Tracking and analyzing the social media data has been contributing as a success parameter for such organizations, however, the data is being poorly harnessed. Therefore, the ethical implications of social media analytics need to be identified and explored for both the organizations and targeted users of social media data. The present work is an exploratory study to identify the various techno-ethical concerns of social media engagement, as well as social media analytics. The impact of these concerns on the individuals, organizations, and society as a whole are discussed. Ethical engagement for the most common social media platforms has been outlined with a number of specific examples to understand the prominent techno-ethical concerns. Both the individual and organizational perspectives have been taken into account to identify the implications of social media analytics.

Social Media Data Analytics for the U.S. Construction Industry: Preliminary Study on Twitter

2017 ◽  
Vol 33 (6) ◽  
pp. 04017038 ◽  
Author(s):  
LiYaning Tang ◽  
Yiming Zhang ◽  
Fei Dai ◽  
Yoojung Yoon ◽  
Yangqiu Song ◽  
...  
Keyword(s):  
Social Media ◽  
Construction Industry ◽  
Data Analytics ◽  
Social Media Data ◽  
Preliminary Study ◽  
Media Data ◽  
The U.S

scholarly journals Journalists’ Use of Social Media to Infer Public Opinion: The citizens’ perspective

2021 ◽  
Author(s):  
Elizabeth Dubois ◽  
Anatoliy Gruzd ◽  
Jenna Jacobson
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Online Survey ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Social Media Platforms ◽  
Use Of Social Media ◽  
Traditional Approaches ◽  
Journalistic Practice ◽  
Media Data

Journalists increasingly use social media data to infer and report public opinion by quoting social media posts, identifying trending topics, and reporting general sentiment. In contrast to traditional approaches of inferring public opinion, citizens are often unaware of how their publicly available social media data is being used and how public opinion is constructed using social media analytics. In this exploratory study based on a census-weighted online survey of Canadian adults (N=1,500), we examine citizens’ perceptions of journalistic use of social media data. We demonstrate that: (1) people find it more appropriate for journalists to use aggregate social media data rather than personally identifiable data; (2) people who use more social media are more likely to positively perceive journalistic use of social media data to infer public opinion; and (3) the frequency of political posting is positively related to acceptance of this emerging journalistic practice, which suggests some citizens want to be heard publicly on social media while others do not. We provide recommendations for journalists on the ethical use of social media data and social media platforms on opt-in functionality.

scholarly journals A Comprehensive Analysis of Approaches for Sentiment Analysis Using Twitter Data on COVID-19 Vaccines

2021 ◽  
Vol 2 (2) ◽  
pp. 1-10
Author(s):  
Amrita Mishra ◽  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Text Classification ◽  
Comprehensive Analysis ◽  
Social Media Data ◽  
The Public ◽  
Opinion Analysis ◽  
Twitter Data ◽  
Media Data

Sentiment Analysis has paved routes for opinion analysis of masses over unrestricted territorial limits. With the advent and growth of social media like Twitter, Facebook, WhatsApp, Snapchat in today’s world, stakeholders and the public often takes to expressing their opinion on them and drawing conclusions. While these social media data are extremely informative and well connected, the major challenge lies in incorporating efficient Text Classification strategies which not only overcomes the unstructured and humongous nature of data but also generates correct polarity of opinions (i.e. positive, negative, and neutral). This paper is a thorough effort to provide a brief study about various approaches to SA including Machine Learning, Lexicon Based, and Automatic Approaches. The paper also highlights the comparison of positive, negative, and neutral tweets of the Sputnik V, Moderna, and Covaxin vaccines used for preventive and emergency use of COVID-19 disease.

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