###### Abstract  
Web surveys are very popular in the Internet space. Web surveys are widely incorporated for gathering customer opinion about Internet services, for sociological and psychological research, and as part of the knowledge testing systems in electronic learning. When conducting web surveys, one of the issues to consider is the respondents' authenticity throughout the entire survey process. We took 20,000 responses to an online questionnaire as experimental data. The survey took about 45 min on average. We did not take into account the given answers; we only considered the response time to the first question on each page of the survey interface, that is, only the users' reaction time was taken into account. Data analysis showed that respondents get used to the interface elements and want to finish a long survey as soon as possible, which leads to quicker reactions. Based on the data, we built two neural network models that identify the records in which the respondent's authenticity was violated or the respondent acted as a random clicker. The amount of data allows us to conclude that the identified dependencies are widely applicable. recation time to interface elements; web surveys; web polls; random clickers; long questionnaires +
Footnote †: journal: Medicalto an hour or even more of a respondent's time. In these circumstances, researchers need tools that allow them to know whether the respondent is tired, the interface is convenient, the participant is a random clicker, or the answer was filled by a bot (script). One of the effective tools for solving the problems of the evaluation of user actions and their verification throughout the web survey is the evaluation of user reactions. Technically, user reaction time is the timespan from the start of the web survey screen representing a page with a number of questions to the time when the user clicked the desired answer option. In works [4; 5; 6] on various experiments with large samples, it was shown that reaction time reflects personal skills of interaction with the user interface. Of course, various factors may influence the change in reaction time, but in general, reactions are maintained the same over the course of a few hours of experimentation. In studies of web surveys, researchers quite often pay attention to the respondent's reaction time. The main focus is on the fact that long reaction times indicate the difficulty of the question [7]. Research in works [8; 9; 10; 11] is dedicated to web questionnaire design and emphasize that the user reaction time should be noted. Works [12; 13; 14] consider reaction time to predict that the respondent will abandon a web survey prior to completing it. It is logical to assume that there is a correlation between the time it takes to read a question and the reaction time; besides, a direct comparison of respondents' reaction times is problematic, because each person has an individual speed [15]. Individual characteristics of a person, according to studies, influence the reaction time quite strongly, which makes it possible to identify the respondent [16; 17; 18; 19; 20; 21; 22]. During the preprocessing of experimental data, it is possible to identify random clickers, who run through web surveys without even reading given questions [23; 24; 25]. Identifying random clickers and excluding them from the dataset is an important task [26], and machine learning is used to solve this problem quite often [27]. Machine learning techniques are used to solve many problems in online survey research [28]. For example, [29] the following methods are considered to describe various relationships between the result and the input data: logistic regression, advanced tree models (decision trees, random forest and boosting), support vector machine and neural network. A significant problem, especially in a child survey, is the substitution of the user during the interview. For example, either a companion or an adult replaces the interviewee in the middle of the survey. Such data should be questioned.