Brand Names as Keywords in Sponsored Search Advertising

The business models of major Web search engines depend on online advertising, primarily in the form of keyword advertising. In recent years, a controversy has gained notoriety worldwide, in both the international court systems and the media. It concerns a form of potential “bait and switch” advertising where a consumer, searching using the brand name of one company, is presented with an advertisement by a competitor of the searched-for brand. We refer to this practice as “piggybacking.” In the U.S. in particular, the legality of this practice, and the potential liability of the search engines for contributing to trademark infringement, is unclear. However, the eventual resolutions of the issue could significantly and negatively impact the business model of Internet search engines. In this paper, we investigate the actual prevalence of piggybacking of major brands in U.S. search engines. We submitted 100 search queries consisting of top global brand names to three major search engines. Analysis of 2,350 advertisements from search engine results pages showed that just 4 percent were triggered by competitors’ trademarked terms. There was even lower use of those trademark terms in the ad text. Thus, overall competitive piggybacking does not appear to be a deceptive or widespread phenomenon. Implications for this are discussed, and suggestions for future research are presented

Problems of Data Science in Organizations: An Explorative Qualitative Analysis of Business Professionals’ Concerns

In this exploratory study, we analyze 150 comments from 79 participants, the roles ranging from top management and other business professionals to software developers, to identify key problems of employing data science in organizations. The comments are retrieved from a publicly available LinkedIn discussion thread in which the participants are discussing the problems relating to data science implementation and management. We use qualitative coding to analyze the comments and find issues from several management-related categories, including (a) job descriptions and recruitment, (b) leadership, (c) economical aspects, and (d) clarity about data use and goals. The findings also highlight that ‘data scientist’ is not just a one role, but combination of many different roles, including analyst, scientist, programmer, and business person. The multiplicity of skills required hinders the recruitment of such individuals, and the existing organizational structures are not always compatible with the multidisciplinary nature of data scientists. We conclude with recommendations to address these issues

Exploring Individual User Attitudes Towards Performance with Web Search Engines: An Extension Study

As the Internet fulfills an increasingly important role in society, study into human behavior and interaction with the technology becomes key to the development of improved systems. As a result, the research agenda of the authors seeks to identify the role of individual differences with users of technology and its subsequent impact on performance. In this initial study, we examine an instance of individual differences with users of the World Wide Web by evaluating user attitudes and performance with Web search engines. Search engine importance is connected to their role as the primary vehicle for locating content on the Internet. Prior research into user attitude has shown a connection with use of technology. In our study we replicate, extend, and critique an investigation conducted by Liaw and Huang (2003) into user attitudes toward search engines as information retrieval tools. Liaw and Huang found that factors such as individual computer experience, quality of search systems, motivation, and perceptions of technology acceptance impact users desire to use search engines as a tool for information retrieval. However, the connection is not drawn to actual individual user performance with a searching task. Based upon the analysis of our data, we were unable to replicate the results achieved in the Liaw and Huang study or draw a connection between these factors and performance. This finding, that our analysis yielded different results, supports the need for further investigation into individual differences and suggests areas for future research

The Effect of Hiding Dislikes on the Use of YouTube's Like and Dislike Features

Using data from a major international news organization, we investigate the effect of hiding the count of dislikes from YouTube viewers on the propensity to use the video like/dislike features. We compare one entire month of videos before (n = 478) and after (n = 394) YouTube began hiding the dislikes counts. Collectively, these videos had received 450,200 likes and 41,892 dislikes. To account for content variability, we analyze the likes/dislikes by sentiment class (positive, neutral, negative). Results of chi-square testing show that while both likes and dislikes decreased after the hiding, dislikes decreased substantially more. We repeat the analysis with four other YouTube news channels in various languages (Arabic, English, French, Spanish) and one non-news organization, with similar results in all but one case. Findings from these multiple organizations suggest that YouTube hiding the number of dislikes from viewers has altered the user-platform interactions for the like/dislike features. Therefore, comparing the like/dislike metrics before and after the removal would give invalid insights into users’ reactions to content on YouTube.© AuthorACM 2022. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in WebSci '22: 14th ACM Web Science Conference 2022, http://dx.doi.org/10.1145/3501247.3531546fi=vertaisarvioitu|en=peerReviewed

Engineers, Aware! Commercial Tools Disagree on Social Media Sentiment : Analyzing the Sentiment Bias of Four Major Tools

Large commercial sentiment analysis tools are often deployed in software engineering due to their ease of use. However, it is not known how accurate these tools are, and whether the sentiment ratings given by one tool agree with those given by another tool. We use two datasets - (1) NEWS consisting of 5,880 news stories and 60K comments from four social media platforms: Twitter, Instagram, YouTube, and Facebook; and (2) IMDB consisting of 7,500 positive and 7,500 negative movie reviews - to investigate the agreement and bias of four widely used sentiment analysis (SA) tools: Microsoft Azure (MS), IBM Watson, Google Cloud, and Amazon Web Services (AWS). We find that the four tools assign the same sentiment on less than half (48.1%) of the analyzed content. We also find that AWS exhibits neutrality bias in both datasets, Google exhibits bi-polarity bias in the NEWS dataset but neutrality bias in the IMDB dataset, and IBM and MS exhibit no clear bias in the NEWS dataset but have bi-polarity bias in the IMDB dataset. Overall, IBM has the highest accuracy relative to the known ground truth in the IMDB dataset. Findings indicate that psycholinguistic features - especially affect, tone, and use of adjectives - explain why the tools disagree. Engineers are urged caution when implementing SA tools for applications, as the tool selection affects the obtained sentiment labels.© Owner/Author(s). ACM 2022. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the ACM on Human-Computer Interaction, https://doi.org/10.1145/3532203.fi=vertaisarvioitu|en=peerReviewed

Finetuning Analytics Information Systems for a Better Understanding of Users : Evidence of Personification Bias on Multiple Digital Channels

Although the effect of hyperparameters on algorithmic outputs is well known in machine learning, the effects of hyperparameters on information systems that produce user or customer segments are relatively unexplored. This research investigates the effect of varying the number of user segments on the personification of user engagement data in a real analytics information system, employing the concept of persona. We increment the number of personas from 5 to 15 for a total of 330 personas and 33 persona generations. We then examine the effect of changing the hyperparameter on the gender, age, nationality, and combined gender-age-nationality representation of the user population. The results show that despite using the same data and algorithm, varying the number of personas strongly biases the information system’s personification of the user population. The hyperparameter selection for the 990 total personas results in an average deviation of 54.5% for gender, 42.9% for age, 28.9% for nationality, and 40.5% for gender-age-nationality. A repeated analysis of two other organizations shows similar results for all attributes. The deviation occurred for all organizations on all platforms for all attributes, as high as 90.9% in some cases. The results imply that decision makers using analytics information systems should be aware of the effect of hyperparameters on the set of user or customer segments they are exposed to. Organizations looking to effectively use persona analytics systems must be wary that altering the number of personas could substantially change the results, leading to drastically different interpretations about the actual user base.© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed