Search engines play an important role in the provision of information. Recently, researchers have raised their concerns about the potential of search engine providers trying to shift the opinion of their users by manipulating search results. But search results might also be manipulated for financial gain. If an increase in User Engagement (UE) can be achieved by manipulating search results, the manipulation of search results can lead to increased advertisement revenue. In this thesis, a 2 x 3 x 3 factorial user study (n = 376) investigating the effect of biased Search Engine Results Pages (SERPs) on UE in users with strong opinions on the topic is presented. Different search result ranking biases were used: a search result ranking biased to agree with the topic, one that disagrees with the topic and one that is indifferent towards the topic. One of two different topics is assigned to the user. The bias in the SERP is determined by the condition assigned to the user (i.e., mitigate, control or reinforce) and the viewpoint of the user towards the topic (i.e., agree or disagree). Furthermore, one out of three queries is presented at the top of the SERP (i.e., a query that agrees, disagrees or is impartial with the topic). It was found that neither mitigating nor reinforcing bias through the SERP affected UE. The exploratory analysis suggests that confirmation bias did not guide the results clicked by the users. Users were not refrained from clicking search results that do not align with their viewpoint on the topic. Instead, the exploratory analysis suggests that results are clicked based on their position (position bias), regardless of whether bias of the user is mitigated or reinforced.

Web-based interaction logging is an important concept for understanding user behavior on web-pages. LogUI is a powerful modern framework for logging a user's interactions. Integrating such a framework in web-pages requires the construction of configuration objects to define selectors that indicate which elements on a web-page should be under observation. Writing such configuration object can be a tedious task for researchers who are less experienced in programming. Therefore, this paper addresses the potential of a graphical user interface (GUI) to simplify the creation of configuration objects. An experimental GUI-based tool was devised to aid a user in the process of producing configuration objects in an interactive fashion. Results from conducting a small scale user study show that users are capable of creating configurations utilizing the GUI-based tool with an average accuracy of 70% measured in terms of selector equivalence. User experience evaluation shows that the tool is perceived as efficient.

Cross Browser Inconsistencies (XBI) were created when different browser vendors implemented their products without deciding upon common protocols for interoperability. It is hard to pinpoint these inconsistencies with precision because of a lack of a good tool. Here we show how to use a web-based event logger (LogUI) to find XBI at a granular level. LogUI attaches itself to the DOM of a webpage and makes a log file based on how the browser re- sponds to user interactions. A test suite is made by simulating user interactions (Selenium WebDriver) to test different browsers and interaction events (individually and in sequences) to generate log files which are then analyzed to spot differences in the actions performed and entries logged. It is found that XBI are few and hard to find, the two XBI found are that browsers load differently and sometimes change the order of actions being performed.

Performing user interface studies and logging user interactions is commonplace in the Interactive Information Retrieval field (IIR). As a result, the LogUI framework was developed to make logging such interactions an easier task. However, this framework does not come with any visualization tool, requiring the researcher to analyze their data separately. We present a generalized data dashboard that allows the researcher to perform basic exploratory analysis on their data, without needing to parse their logging data themselves. We gathered a set of important logging metrics from existing user interface experiments and based our dashboard design on these metrics. Finally, we verified the usability and effectiveness of the dashboard by interviewing a set of IIR researchers and show that we developed an effective dashboard.