An Empirical Investigation On Search Engine Ad Disclosure

This representative study of German search engine users (N=1,000) focuses on the ability of users to distinguish between organic results and advertisements on Google results pages. We combine questions about Google's business with task-based studies in which users were asked to distinguish between ads and organic results in screenshots of results pages. We find that only a small percentage of users is able to reliably distinguish between ads and organic results, and that user knowledge of Google's business model is very limited. We conclude that ads are insufficiently labelled as such, and that many users may click on ads assuming that they are selecting organic results

Constructing an Interaction Behavior Model for Web Image Search

User interaction behavior is a valuable source of implicit relevance feedback. In Web image search a different type of search result presentation is used than in general Web search, which leads to different interaction mechanisms and user behavior. For example, image search results are self-contained, so that users do not need to click the results to view the landing page as in general Web search, which generates sparse click data. Also, two-dimensional result placement instead of a linear result list makes browsing behaviors more complex. Thus, it is hard to apply standard user behavior models (e.g., click models) developed for general Web search to Web image search. In this paper, we conduct a comprehensive image search user behavior analysis using data from a lab-based user study as well as data from a commercial search log. We then propose a novel interaction behavior model, called grid-based user browsing model (GUBM), whose design is motivated by observations from our data analysis. GUBM can both capture users' interaction behavior, including cursor hovering, and alleviate position bias. The advantages of GUBM are two-fold: (1) It is based on an unsupervised learning method and does not need manually annotated data for training. (2) It is based on user interaction features on search engine result pages (SERPs) and is easily transferable to other scenarios that have a grid-based interface such as video search engines. We conduct extensive experiments to test the performance of our model using a large-scale commercial image search log. Experimental results show that in terms of behavior prediction (perplexity), and topical relevance and image quality (normalized discounted cumulative gain (NDCG)), GUBM outperforms state-of-the-art baseline models as well as the original ranking. We make the implementation of GUBM and related datasets publicly available for future studies.Comment: 10 page

Do You See It Clearly? The Effect of Packaging and Label Format on Google Ads

The nature of e-commerce prevents the perception of the intrinsic and sensory attributes of wine. In the virtual environment, visual cues allow consumers to perceive the product, determine their attitude and form a preference. Users will choose one product or another based on the visual appeal of the advertisements they have seen. Wine marketers must consider the importance of the advertisement elements and attract the consumer's attention. Optimizing the elements included in these messages can help capture consumers' attention and achieve a higher click-through rate on the ads. The main objective of this work is to analyse the awareness that different advertisements achieve. Specifically, we use a 2 x 2 x 2 experimental design where we manipulate the packaging format (single bottle vs. pack), labelling (bottle without label vs. labelled bottle) for wine ads (white and red). To analyse attention, we used an eye-tracking methodology. The main results suggest that attention is captured more quickly with an individual bottle without a label than with a particular bottle with a label in Google ads. However, ads showing packs of bottles with labels get more attention than ads using packs of bottles without labels.The University of Cadiz funded this Research, grant number PR2017-039 of Plan Propio Project and was supported by the Institute of Research and Development Social and Sustainability (INDESS)

An Exploration of User Engagement With a Search Assistance Tool in Different Positions on a SERP.

This study aimed to explore the difference in user engagement with a search assistance tool in different positions on a SERP. A usability study with eye-tracker was conducted in a lab environment. Overall, there were 12 subjects participated in this study, each of them was asked to perform two tasks on a search system with a search assistance tool placed in two positions. Qualitative data collected from the retrospective interview and quantitative data gathered from questionnaires, eye-tracking system, and custom log system were analyzed to investigate the position effect. The results in this study showed that the search assistance tool placed in the middle is easier to get noticed while people are more likely to pay attention to it and use it when the search assistance tool is placed on the right side of the page. Also, the source authority and the information foraging theories like Camouflage Links, Banner Blindness have impacts on the use of the search assistance tool.Master of Science in Information Scienc

EYE-AS-AN-INPUT FOR IMPROVING INTERACTIVE INFORMATION RETRIEVAL

In this work, Publication Access Through Tiered Interaction and Exploration (PATTIE) is presented with the eye as an additional input modality. PATTIE is built upon the scatter/gather information retrieval paradigm where users can explore a visual and interactive table-of-contents metaphor for large-scale document collections in an iterative manner. Additionally, the prototype has been integrated with eye-tracking through the web camera and experimental findings are provided to demonstrate a proof-of-concept for interest modeling at the term level and implicit relevance feedback on the gold standard inaugural 2019 Text REtrieval Conference Precision Medicine dataset (TREC PM). Low error rates for gaze tracking, and acceptable performance on binary classification of interest are reported as well as statistically significant increases in precision and recall performance for relevant information on a TREC PM task when PATTIE is used with eye-as-an-input versus a baseline PATTIE system.Doctor of Philosoph

Users, Queries, and Bad Abandonment in Web Search

After a user submits a query and receives a list of search results, the user may abandon their query without clicking on any of the search results. A bad query abandonment is when a searcher abandons the SERP because they were dissatisfied with the quality of the search results, often making the user reformulate their query in the hope of receiving better search results. As we move closer to understanding when and what causes a user to abandon their query under different qualities of search results, we move forward in an overall understanding of user behavior with search engines. In this thesis, we describe three user studies to investigate bad query abandonment. First, we report on a study to investigate the rate and time at which users abandon their queries at different levels of search quality. We had users search for answers to questions, but showed users manipulated SERPs that contain one relevant document placed at different ranks. We show that as the quality of search results decreases, the probability of abandonment increases, and that users quickly decide to abandon their queries. Users make their decisions fast, but not all users are the same. We show that there appear to be two types of users that behave differently, with one group more likely to abandon their query and are quicker in finding answers than the group less likely to abandon their query. Second, we describe an eye-tracking experiment that focuses on understanding possible causes of users' willingness to examine SERPs and what motivates users to continue or discontinue their examination. Using eye-tracking data, we found that a user deciding to abandon a query is best understood by the user's examination pattern not including a relevant search result. If a user sees a relevant result, they are very likely to click it. However, users' examination of results are different and may be influenced by other factors. The key factors we found are the rank of search results, the user type, and the query quality. For example, we show that regardless of where the relevant document is placed in the SERP, the type of query submitted affects examination, and if a user enters an ambiguous query, they are likely to examine fewer results. Third, we show how the nature of non-relevant material affects users' willingness to further explore a ranked list of search results. We constructed and showed participants manipulated SERPs with different types of non-relevant documents. We found that user examination of search results and time to query abandonment is influenced by the coherence and type of non-relevant documents included in the SERP. For SERPs coherent on off-topic results, users spend the least amount of time before abandoning and are less likely to request to view more results. The time they spend increases as the SERP quality improves, and users are more likely to request to view more results when the SERP contains diversified non-relevant results on multiple subtopics

Information between Data and Knowledge: Information Science and its Neighbors from Data Science to Digital Humanities

Digital humanities as well as data science as neighboring fields pose new challenges and opportunities for information science. The recent focus on data in the context of big data and deep learning brings along new tasks for information scientist for example in research data management. At the same time, information behavior changes in the light of the increasing digital availability of information in academia as well as in everyday life. In this volume, contributions from various fields like information behavior and information literacy, information retrieval, digital humanities, knowledge representation, emerging technologies, and information infrastructure showcase the development of information science research in recent years. Topics as diverse as social media analytics, fake news on Facebook, collaborative search practices, open educational resources or recent developments in research data management are some of the highlights of this volume. For more than 30 years, the International Symposium of Information Science has been the venue for bringing together information scientists from the German speaking countries. In addition to the regular scientific contributions, six of the best competitors for the prize for the best information science master thesis present their work