Click tracking
[2] However, as technology develops, new software allows for in depth analysis of user click behavior using hypervideo tools.
[1] Given that the internet can be considered a risky environment, research strives to understand why users click certain links and not others.
[5][6] Click tracking is relevant in several industries including Human-Computer Interaction (HCI), software engineering, and advertising.
[9] Click tracking employs many modern techniques such as machine learning and data mining.
Examples of TRTs include radio frequency identification (RFID), credit cards, and store video cameras.
The experience is also difficult because users have to first imagine how to complete the task using keyboard and cursor features and then employ gaze.
[12] However, in order to track user eye movements, a lab setting with appropriate equipment is often required.
[13] User browsing behavior is often tracked using server access logs which contain patterns of clicked URLs, queries, and paths.
The collected mouse data can be used to create videos, allowing for user behavior to be replayed and easily analyzed.
Hypermedia is used to create such visualizations that allow for behavior like highlighting, hesitating, and selecting to be monitored.
[1] In a search session, users can be identified using cookies, identd protocol, or their IP address.
[2] Cookies are added to HTTP (Hypertext Transfer Protocol), and when a user clicks on a link, they are connected to the associated web server.
[15] When users have a personal connection to a subject matter they tend to click that article more frequently.
Pictures, position, and specific individuals in the news content also more heavily influenced users’ decisions.
[8] The internet can be considered a risky environment due to the abundance of cybersecurity attacks that can occur and the prevalence of malware.
[4] Users were also found to better recognize malware risks when there is a greater potential for revealing their personal information.
Certain user actions on a webpage that can be used as a part of the interpretation process include bookmarking, saving, or printing a particular web page.
[9] Through collecting click data from a few individuals, the relevance of results for all users for given queries can improve.
Click data outside of search sessions can also be used to improve the accuracy of relevant results for users.
Then, live user click feedback in the form of tracked click-through rates (CTR) in search sessions can be used to rerank the results based on the data.
[17] Eye-tracking research indicates that users exhibit an abundance of non-sequential viewing activity when looking at search results.
[18] Huang et al. defines strategic customers as “forward looking” individuals who know that their clicks are being tracked and expect that companies will engage in appropriate business activities.
In the conducted study, researchers used clickstream data from customers to observe their preferences and desired product quantities.
In the 2012 Fraud Detection in Mobile Advertising (FDMA) conference, competition teams were tasked with having to use data mining and machine learning techniques to determine “fraudulent publishers” from a given dataset.
[20] Researchers studied how the Email Mining Toolkit (EMT) could be used to detect viruses by studying such user email account behavior and found that it was easier to decipher quick, broad viral propagations in comparison to slow, gradual viral propagations.
Many third party email trackers are also involved in web tracking, leading to further user profiling.
They also observe giving users control over how that information is represented in databases in the realm of trajectory data, and they create a system that allows for this approach.
[5] Karegar et al. compares the simple agree/disagree format with forms that incorporate checkboxes, drag and drop (DAD), and swipe features.
