Tracing the Legitimacy of Artificial Intelligence – A Media Analysis, 1980-2020

2022 ◽  
Author(s):  
Ekaterina Korneeva ◽  
Torsten Oliver Salge
Keyword(s):  
Artificial Intelligence ◽  
Media Analysis

scholarly journals Population health AI researchers’ perceptions of the public portrayal of AI: A pilot study

2020 ◽  
pp. 096366252096549
Author(s):  
Gabrielle Samuel ◽  
Heilien Diedericks ◽  
Gemma Derrick
Keyword(s):  
Artificial Intelligence ◽  
Population Health ◽  
Scientific Practice ◽  
Media Analysis ◽  
The Public ◽  
Canadian Population ◽  
The Media ◽  
Epistemic Modesty ◽  
Artificial Intelligence Systems ◽  
Research And Policy

This article reports how 18 UK and Canadian population health artificial intelligence researchers in Higher Education Institutions perceive the use of artificial intelligence systems in their research, and how this compares with their perceptions about the media portrayal of artificial intelligence systems. This is triangulated with a small scoping analysis of how UK and Canadian news articles portray artificial intelligence systems associated with health research and care. Interviewees had concerns about what they perceived as sensationalist reporting of artificial intelligence systems – a finding reflected in the media analysis. In line with Pickersgill’s concept of ‘epistemic modesty’, they considered artificial intelligence systems better perceived as non-exceptionalist methodological tools that were uncertain and unexciting. Adopting ‘epistemic modesty’ was sometimes hindered by stakeholders to whom the research is disseminated, who may be less interested in hearing about the uncertainties of scientific practice, having implications on both research and policy.

scholarly journals Creating Brand Image Profile by Social Media Analysis

2021 ◽  
Vol 11 (2) ◽  
pp. 8-15
Author(s):  
İbrahim Sabuncu ◽  
Berivan Edeş ◽  
Doruk Sıtkıbütün ◽  
İlayda Girgin ◽  
Kadir Zehir
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Sentiment Analysis ◽  
Brand Image ◽  
Media Analysis ◽  
Alternative Methods ◽  
Social Media Analysis ◽  
Focus Group Study ◽  
Complementary Method ◽  
Mining Tool

The purpose of creating a brand image profile is to measure the brand perception of consumers considering brand attributes. Thus, marketing decisions can be made based on the brand's strengths and weaknesses by determining them. The brand image profile is traditionally created using the attitude scales and surveys. However, alternative methods are needed since the questionnaires' responses are careless, the number of participants is relatively low and the cost per participant is high. In this study, as an alternative method, creating a brand image profile by analyzing social media data with artificial intelligence was made for the iPhone product. Firstly, the focus group study determined the attributes related to the last version of the iPhone. Then, between December 17th, 2019 and March 23rd, 2020, 87.227 tweets that include these attributes in English were collected from the Twitter social media platform through the RapidMiner data mining tool. Sentiment analysis was performed on collected tweets by the MeaningCloud text mining tool. In this analysis, positive and negative emotions were tried to be detected through artificial intelligence algorithms. Net Brand Reputation Score (NBR) was calculated using the positive and negative tweets amount for each attribute separately. Brand image profile was created by skew analysis using NBR values. As a result, it is thought that social media analysis can be a complementary method that can be used with traditional methods in creating a brand image profile. So, it is seen as an inevitable method to use in further studies to make sentiment analysis by processing raw data received from the Social Media platforms through artificial intelligence algorithms to transform the product label or the perspectives of an event into meaningful information.

scholarly journals Artificial intelligence-enabled analysis of UK and US public attitudes on Twitter and Facebook towards COVID-19 vaccination (Preprint)

2020 ◽  
Author(s):  
Amir Hussain ◽  
Ahsen Tahir ◽  
Zain Hussain ◽  
Zakariya Sheikh ◽  
Kia Dashtipour ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Language Processing ◽  
Vaccine Development ◽  
Public Attitudes ◽  
Public Attitude ◽  
Media Analysis ◽  
Public Confidence ◽  
The Us ◽  
The Uk

UNSTRUCTURED Background: Global efforts towards the development and deployment of a vaccine for SARS-CoV-2 are rapidly advancing. We developed and applied an artificial-intelligence (AI)-based approach to analyse social-media public sentiment in the UK and the US towards COVID-19 vaccinations, to understand public attitude and identify topics of concern. Methods: Over 300,000 social-media posts related to COVID-19 vaccinations were extracted, including 23,571 Facebook-posts from the UK and 144,864 from the US, along with 40,268 tweets from the UK and 98,385 from the US respectively, from 1st March - 22nd November 2020. We used natural-language processing and deep learning-based techniques to predict average sentiments, sentiment trends and topics of discussion. These were analysed longitudinally and geo-spatially, and a manual- eading of randomly selected posts around points of interest helped identify underlying themes and validated insights from the analysis. Results: We found overall averaged positive, negative and neutral sentiment in the UK to be 58%, 22% and 17%, compared to 56%, 24% and 18% in the US, respectively. Public optimism over vaccine development, effectiveness and trials as well as concerns over safety, economic viability and corporation control were identified. We compared our findings to national surveys in both countries and found them to correlate broadly. Conclusions: AI-enabled social-media analysis should be considered for adoption by institutions and governments, alongside surveys and other conventional methods of assessing public attitude. This could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccinations, help address concerns of vaccine-sceptics and develop more effective policies and communication strategies to maximise uptake.

scholarly journals The Impact of Artificial Intelligence on Branding

2021 ◽  
Vol 29 (4) ◽  
pp. 221-246
Author(s):  
Varsha P. S. ◽  
Shahriar Akter ◽  
Amit Kumar ◽  
Saikat Gochhait ◽  
Basanna Patagundi
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Citation Analysis ◽  
Brand Choice ◽  
Media Analysis ◽  
Social Media Analytics ◽  
Information Cascades ◽  
Luxury Brands ◽  
Value Propositions ◽  
The Impact

Understanding the growth paths of artificial intelligence (AI) and its impact on branding is extremely pertinent of technology-driven marketing. This explorative research covers a complete bibliometric analysis of the impact of AI on branding. The sample for this research included all 117 articles from the period of 1982-2019 in the Scopus database. A bibliometric study was conducted using co-occurrence, citation analysis and co-citation analysis. The empirical analysis investigates the value propositions of AI on branding. The study revealed the nine clusters of co-occurrence: Social Media Analytics and Brand Equity; Neural Networks and Brand Choice; Chat Bots-Brand Intimacy; Twitter, Facebook, Instagram-Luxury Brands; Interactive Agent-Brand Love and User Choice; Algorithm Recommendations and E-Brand Experience; User-Generated Content-Brand Sustainability; Brand Intelligence Analytics; and Digital Innovations and Brand Excellence. The findings also identify four clusters of citation analysis—Social Media Analysis and Brand Photos, Network Analysis and E-Commerce, Hybrid Simulating Modelling, and Real-time Knowledge-Based Systems—and four clusters of co-citation analysis: B2B Technology Brands, AI Fostered E-Brands, Information Cascades and Online Brand Ratings, and Voice Assistants-Brand Eureka Moments. Overall, the study presents the patterns of convergence and divergence of themes, narrowing to the specific topic, and multidisciplinary engagement in research, thus offering the recent insights in the field of AI on branding.

scholarly journals Artificial intelligence-enabled analysis of UK and US public attitudes on Facebook and Twitter towards COVID-19 vaccinations

2020 ◽  
Author(s):  
Amir Hussain ◽  
Ahsen Tahir ◽  
Zain Hussain ◽  
Zakariya Sheikh ◽  
Mandar Gogate ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Language Processing ◽  
Vaccine Development ◽  
Public Attitudes ◽  
Public Attitude ◽  
Media Analysis ◽  
Public Confidence ◽  
The Us ◽  
The Uk

AbstractBackgroundGlobal efforts towards the development and deployment of a vaccine for SARS-CoV-2 are rapidly advancing. We developed and applied an artificial-intelligence (AI)-based approach to analyse social-media public sentiment in the UK and the US towards COVID-19 vaccinations, to understand public attitude and identify topics of concern.MethodsOver 300,000 social-media posts related to COVID-19 vaccinations were extracted, including 23,571 Facebook-posts from the UK and 144,864 from the US, along with 40,268 tweets from the UK and 98,385 from the US respectively, from 1st March - 22nd November 2020. We used natural language processing and deep learning based techniques to predict average sentiments, sentiment trends and topics of discussion. These were analysed longitudinally and geo-spatially, and a manual reading of randomly selected posts around points of interest helped identify underlying themes and validated insights from the analysis.ResultsWe found overall averaged positive, negative and neutral sentiment in the UK to be 58%, 22% and 17%, compared to 56%, 24% and 18% in the US, respectively. Public optimism over vaccine development, effectiveness and trials as well as concerns over safety, economic viability and corporation control were identified. We compared our findings to national surveys in both countries and found them to correlate broadly.ConclusionsAI-enabled social-media analysis should be considered for adoption by institutions and governments, alongside surveys and other conventional methods of assessing public attitude. This could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccinations, help address concerns of vaccine-sceptics and develop more effective policies and communication strategies to maximise uptake.

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