Click tracking

Tracking and recording technologies (TRTs) can be split into two categories, institutional TRTs and end-user TRTs. Institutional TRTs and end-user TRTs differ by who is collecting and storing the data, and this can be respectively understood as institutions and users. Examples of TRTs include radio frequency identification (RFID), credit cards, and store video cameras. Research suggests that individuals are concerned with privacy, but they are less concerned with how TRTs are used daily. Systems that employ gaze-tracking often try to mimic cursor and keyboard behavior. However, in order to track user eye movements, a lab setting with appropriate equipment is often required. Mouse and keyboard activity can be measured remotely, so this quality can be capitalized for usability testing. In this process, information about a user is collected from their web browser to create a browser fingerprint. A browser fingerprint contains information about a device, its operating system, its browser, and its configuration. HTTP headers, JavaScript, and browser plugins can be used to build a fingerprint. Browser fingerprints can change over time from automatic software updates or user browser preference adjustments. Measures to increase privacy in this realm can reduce functionality by blocking features. == Methods of click tracking ==

User browsing behavior is often tracked using server access logs which contain patterns of clicked URLs, queries, and paths. These tools create a visual from user click data on a webpage. ClickHeat and Crazy Egg showcase the density of user clicks using specific colors, and all of these tools allow for webpage visitors to be categorized into groups by qualities like being a mobile user or using a particular browser. The specific groups' data can be analyzed for further insight. == Click behaviour ==

One of the main factors users consider when clicking links is a link's position in a list of results. The closer links are to the top, the more likely they are to be selected by users. 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. The source of the news was deemed as less important. Click attitude and click intention play a large role in user click behavior. In one study when research participants were presented with positive and negative insurance advertisement photographs, emotion was seen to have a positive association with click intention and click attitude. The researchers also observed that click attitude affects click intention, and positive emotion has more of an impact than negative emotion on click attitude. The internet can be considered a risky environment due to the abundance of cybersecurity attacks that can occur and the prevalence of malware. Hence, whenever individuals use the internet, they have to decide whether or not to click on the various links. A 2018 study found that users tend to click on more URLs on websites they are familiar with; this user trait is then exploited by cybercriminals, and personal information can be compromised. Hence, trust is seen to also increase click-through intention. When given Google Chrome warnings, 70% of the time people will click through. They also tend to adjust default computer settings in this process. Users were also found to better recognize malware risks when there is a greater potential for revealing their personal information. == Relevance of search results ==

Pages that are viewed by users during a particular search session constitute click data. This is because users tend to select links that are at the top of result lists. However, this position does not mean a result is the most relevant since relevance can change over time. As a part of a machine learning approach to improving the result order, human editors begin by supplying an original rank for each result to the algorithm. 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. Click dwell time is how long a user takes to return to the search engine results page (SERP) after clicking on a particular result, and this can indicate how satisfied the user is with a particular result. == Extensions ==

Advertising Supply-demand mismatch costs can be reduced through click tracking. 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. Noisy clicks are when customers click but do not actually buy the product. This leads to imperfect advanced demand information or ADI. Spear-phishing is a more “targeted” form of phishing in which user information is used to personalize emails and entice users to click. This differs from user email account behavior because users tend to have a particular network they communicate with regularly. By merely opening an email, users' email addresses can be leaked to third parties, and if users click on links within the emails, their email address can get leaked to a larger number of third parties. Third-party tracking generates more privacy concerns than first-party tracking because it allows for many website or application records about a particular user to be combined, yielding better user profiles. Binns et al. found that among 5000 popular websites, the top two websites alone had 2000 trackers. Of the 2000 embedded trackers, 253 were used in 25 other websites. Researchers evaluated the reach of third-party trackers based on their contact with users rather than websites, so more "popular" trackers were those who received information about the highest number of people rather than code embedded in the most first-parties. Google and Facebook were deemed as the first and second largest web trackers, and Google and Twitter were deemed as the first and second largest mobile trackers. == See also ==