SINCE THE USE OF INTERNET HAS INCREDIBLY INCREASED, IT BECOMES AN IMPORTANT SOURCE OF KNOWLEDGE ABOUT ANYTHING FOR EVERYONE. THEREFORE, THE ROLE OF Search Engine AS AN EFFECTIVE APPROACH TO FIND INFORMATION IS CRITICAL FOR INTERNET'S USERS. THE STUDY OF Search Engine USERS' BEHAVIOR HAS ATTRACTED CONSIDERABLE RESearch ATTENTION. THESE STUDIES ARE HELPFUL IN DEVELOPING MORE EFFECTIVE Search Engine AND ARE USEFUL IN THREE POINTS OF VIEW: FOR USERS AT THE PERSONAL LEVEL, FOR Search Engine VENDORS AT THE BUSINESS LEVEL, AND FOR GOVERNMENT AND MARKETING AT SOCIAL SOCIETY LEVEL. THESE KINDS OF STUDIES CAN BE DONE THROUGH ANALYZING THE LOG FILE OF Search Engine WHEREIN THE INTERACTIONS BETWEEN Search Engine AND THE USERS ARE CAPTURED. IN THIS PAPER, WE AIM TO PRESENT ANALYSES ON THE QUERY LOG OF A WELL-KNOWN AND MOST USED PERSIAN Search Engine. OUR ANALYSES ARE PRESENTED IN THREE MAIN CATEGORIES: 1) STATS-BASED ANALYSES, 2) TEMPORAL-BASED ANALYSES, AND 3) TOPIC-BASED ANALYSES. THE OBTAINED RESULTS ARE PROMISING. MOBILE USERS OFTEN POSTED QUERIES IN WEEKENDS, WHEREAS WEB USERS UTILIZE THE Search Engine IN WORKWEEKS. THE MAJORITY OF QUERIES POSTED FORM MOST-POPULATED CITIES. ADDITIONALLY, IRANIANS ARE MOSTLY INTERESTED IN POLITICAL, SOCIAL, AND ECONOMICAL TOPICS.

ReSearch on Search Engines has demonstrated their significant impact on information Search and user behavior and highlighted the need for continued investigation of their effectiveness, efficiency, and ethical implications. Moreover, Search Engine optimization (SEO) has emerged as a crucial aspect of online content creation and marketing, as it involves optimizing websites and online materials to improve their visibility and ranking on Search Engine results pages, thereby increasing their reach and impact. SEO metrics provide valuable insight into a website's Search Engine performance. However, the relative importance of these metrics remains unclear as Search Engine algorithms and keyword competitiveness can vary widely. Therefore, ongoing reSearch is needed to better understand the relationship between SEO metrics and Search Engine rankings. Given the challenge of determining the relative importance of SEO metrics using traditional statistical methods, interested in applying machine learning techniques. By training models with custom datasets of three Search Engine results (Google, Bing, and DuckDuckGo) and their associated SEO metrics, we hope to gain a better understanding of the complex relationships between these variables. The first two pages of each Search Engine return the most relevant results. In this article, we propose two types of methods to do this. First, classify the page index of web Search results. Second, learning ranking methods to rank them. LambdaRank has the best NDCG compared to other methods for Google and DuckDuckGo which are 0. 86 and 0. 93 respectively. RankNet is the best learning-to-rank method for Bing with 0. 85 as the NDCG value. Logistic regression has the highest accuracy compared to other methods in page index classification for Bing and Google, which are 61. 29% and 64. 71%, respectively. K-nearest neighbor performs best for DuckDuckGo with an accuracy of 66. 67%.

IN THE FIELD OF GEOGRAPHIC INFORMATION SCIENCE SPATIOTEMPORAL INFORMATION EXTRACTION FROM WEB PAGES, ESPECIALLY UNSTRUCTURED DOCUMENTS, IS ONE OF THE GROWING AREAS OF THE RESearch. ABUNDANT NEWS IS PUBLISHING EVERY HOUR ON THE WEB, WHICH CONTAINS VALUABLE SPATIOTEMPORAL INFORMATION FOR ITS USERS. IT IS CUMBERSOME AND TIME-CONSUMING TO Search AMONG UNSTRUCTURED TEXTS AND FIND EVENTS OF INTEREST. IN THIS WORK, WE WILL SHOW YOU HOW TO EXTRACT SPATIOTEMPORAL AND SEMANTIC ENTITIES AND RELATIONSHIPS REPRESENTING IN CULTURAL EVENT NEWS REPORTS AND Search WITHIN INFORMATION. NATURAL LANGUAGE PROCESSING (NLP) AND AUTOMATIC ONTOLOGY POPULATION ARE TIGHTLY COUPLED, AND TOGETHER THEY MAKE IT POSSIBLE TO HAVE WEB DOCUMENTS SEMANTICALLY SO THAT NOT ONLY CAN MACHINES COMPREHEND THE WEB DOCUMENTS, BUT ALSO AS A RESULT, USERS ARE ABLE TO FIND THE IDEAL INFORMATION WITH EASE. A SPATIOTEMPORAL SEMANTIC Search Engine ENABLES US TO ANSWER, WHERE AND WHEN AN EVENT WILL TAKE PLACE.

IN THIS PAPER WE INVESTIGATE A PERSIAN Search Engine LOG AND PRESENT A COMPREHENSIVE ANALYSIS OF QUESTION QUERIES IN THREE LEVELS: STRUCTURE, CLICK AND TOPIC. BY ANALYZING QUESTION QUERIES CHARACTERISTICS, WE EXPLORE BEHAVIOR OF PERSIAN LANGUAGE USERS. OUR EXPERIMENTAL RESULTS SHOW THAT QUESTION QUERIES LENGTH ARE LARGER THAN NORMAL QUERIES. MOST OF THESE QUERIES CONTAINED QUESTION WORDS "HOW" AND "WHAT" AND THEIR TOPICS WERE MAINLY ABOUT HEALTH, POLICY, RELIGION AND SOCIETY.