The main objective of this study was to investigate whether searching firm's ticker symbol and name in Google can predict its future stock market activities. In so doing, the data related to firms' ticker symbol and firms' name were collected using Google Trend and stock market activity was measured using four proxies, namely abnormal return, return volatility, stock trading volume and stock trading count. In order to meet the main objective of the study, multiple regression and panel regression were used over13082 firmmonth observation during the years between 2005 and 2018. The results showed that the future market activity, including return volatility, stock trading volume and stock trading count, increased with searching the firms' ticker and name in Google. However, there was no significant relationship between the future abnormal return and Google searches. Findings also showed that future market activity can be predicted using Google searches. In addition, there was a more significant relationship between the searched firms' ticker symbol than the searched firms' name and future market activity.

The aim off this research was to investigate user's query expansion in Google search Engine based on the "Cognitive Load Theory" and the extent and types of cognitive loads resulted from suggested keywords/queries presented in Google search engine. In this article, data were collected from 60 postgraduate students in Humanities/Social Sciences and Basic and Applied Sciences - at Ferdowsi University of Mashhad, Iran. Non- probability sampling - especially purposive sampling- was used as the sampling method. Also, individual sessions were administered with each of the students. Findings show that suggested keywords/queries and the retrieved pages do not make any significant problem (or negative cognitive load) for users. Also, there is no significant difference between students of the two fields of study regarding cognitive load in the query expansion process in Google. Approximately most of the suggested keywords/queries and the retrieved pages matched with users' needs and had direct relationship with their initial keywords. On the whole, the suggested keywords and queries have taken an appropriate approach regading more relevant results in this search engine.

Objective (s): Traditional health surveillance systems usually publish reports of infectious disease outbreaks 1 to 2 weeks after onset. The Google Trends tool shows people search information with a one-day delay. These data can be used to identify and manage epidemics of infectious diseases. The aim of this study was to predict the Covid-19 epidemic using the Google Trends. Methods: This descriptive cross-sectional study was conducted in February 2021. Google Trends data was used to determine how much attention was paid to COVID-19. Data on COVID-19 deaths were obtained from the Iran Ministry of Health. Data were collected and reviewed in the period from February 22, 2020 to January 20, 2021. Data were analyzed using Excel and SPSS soft wares. Results: Simultaneously with the announcement of incidence of COVID-19 in Iran on February 20, the society’ s sensitivity to COVID-19 has increased and the search rate for COVID-19 in Google has reached its maximum. Three waves of COVID-19 outbreak have been observed in Iran by the end of December 2020. These three waves were similarly observed in Google trends. In all three COVID-19 waves, the peak of Google search occurred 10 to 20 days before the peak of the number of deaths. Conclusion: The Google Trends tool can detect the COVID-19 outbreak quicker. Google search data can be used as a complement to the infectious disease surveillance system.

This comparative study addressed user satisfaction of Unassisted Keyword Search Paradigm with Assisted Keyword Search Paradigm in the Google search engine. The methodology was one group pretest-posttest design. The instrument for data collection was 3 researcher-made questionnaires that one of them included closed questions about expertise in using computer and the Internet as well as demographic information.Other questionnaires were process-based forms which dealt with search questions, search procedures and evaluation forms for each state. The subjects under study were postgraduate students of Foreign Languages and Linguistic Department of Shiraz University, 30 students were selected by using the propotional random sampling technique and non- random technique was used for whom didn’t participate.Findings of the study revealed that about 70 percent of suggestions presented in Google Suggest Beta as an assisted keyword search server had a middle-to-higher relevance. Although there was no significant difference between precision based user oriented relevance in both of paradigms, assisted keyword search led to a significant increase in precision on based system oriented relevance. Generally, the second paradigm caused a significant increase in both the ease of use and the whole performance of the system in finding expected results .Although there was not seen any significant difference between time satisfaction and search results' value, the total user satisfaction increased significantly in the second paradigm. Finally, some suggestions for futher studies were provided.

Purpose: Evaluating the Fuzzy and classical search engines' performance in Persian information retrieval to determine the false drop rate and select the best in retrieving the lowest duplicate records. Methodology: In this applied research, the semi-experimental, evaluative and comparative methods are adopted. Research samples are selected according to purposeful sampling, based on the popularity of search engines. The data collection tool is a researcher-devised checklist with 20 questions. Findings: It is revealed that Google out performs Yahoo and Bing in both the fuzzy and classical evaluations. The obtained precision ratio of the fuzzy evaluation of search engines is greater than of the classical evaluation. In both evaluations, Google, Yahoo and Bing have the lowest rate of false drop, respectively. In the fuzzy evaluation, this false drop ratio is less than of the classical evaluation. Google has the lowest and Yahoo has the highest duplicate records' count. Conclusion: The obtained findings from the fuzzy and classical evaluation reveal that Fuzzy evaluation increases the precision rate and reduces false drop in search engines. Moreover the Fuzzy evaluation provides a more accurate and realistic precision and false drop rate by introducing a spectrum of relevance rate of retrieved records. It is recommended that researchers apply fuzzy evaluation when evaluating the search engines' performance. In general, Google has better performance than Bing and Yahoo based on three measured criteria. Consequently, users are advised to apply this search engine when searching for Persian information on the web to save time and money.

Purpose: The purpose of the present study is to investigate the Impact of Concept-based Image Indexing on Image Retrieval via Google. Due to the importance of images, this article focuses on the features taken into account by Google in retrieving the images. Methodology: The present study is a type of applied research, and the research method used in it comes from quasi-experimental and technology-based methods. Findings: 900 images with concept-based characteristics were uploaded on iiproject.ir domain. Google retrieved 417 images of 900 ones that are used in this study. In 4 codes of “image title”, “Alt text”, “property”, and images with “Q code”, no images were retrieved, so the analysis is done on the rest of 5 codes, “image caption in English”, “image caption in Farsi”, “file name”, “Controlled language” and “free language”. Paying attention to these components in uploading images on websites causes Google retrieve more images. The Chi-square test for difference of retrieved images in 5 Cods is significant, and revealed that, in different codes, significantly various numbers of images were retrieved. Caption allocation in English has the best effect on retrieving images in the study sample, while the assignation of the file name is less effective in image retrieval ranking. The Kruskal-Wallis test to assess the group differences in 5 codes is significant. It means the average of group differences across 5 codes is significant. Originality/Value: This paper tries to introduce the main elements that a search engine such as Google may consider in the indexing and retrieval of images.

Nowadays, Bitcoin is one of the most important cryptocurrencies that has the largest volume of exchanges in the cryptocurrency market and between businesses. The feature of the possibility of online payments between individuals and businesses directly and without referring to the financial institution has made the price of these cryptocurrencies important for businesses and traders and the basis for decision making. Therefore, the issue of price predictability is an important issue that can be affected by search volume. The purpose of this research is studying and investigating the relationship between the volume of Internet searches and its effect on the price of these cryptocurrencies. In addition, another goal of this article is to introduce Google Trends (GT) as a tool for accessing big data for business researches. The required data was extracted from Google Trends in the period 2016 to 2021. The volume of data was 5742 and the whole statistical population was used. The research method is descriptive-exploratory with the aim of explaining the relationship between "Google search volume index" and "bitcoin price". Data were analyzed using Spearman correlation test. Findings indicate a strong and very strong relationship between the studied indicators, which is explained.

INTRUDUCTION: In recent years, some universities and research institutions have built institutional repositories. These repositories save, preserve and give access to research works authored by students and researchers. As access to repository resources through search engines is important, indexing and accessibility of repository resources of Iranian universities of medical sciences were compared in this study. METHODOLOGY: The study population included resources available in the organizational repositories of Iranian universities of medical sciences. These repositories were identified through the Directory of Open Access Repositories. In this directory, there were 9 repositories belonged to Iranian universities of medical sciences. Access to 2 repositories was not possible at the time of data collection, and resources were collected from 7 reminded repositories. The sample size was determined based on Morgan Table. Sampling was implemented through simple stratified sampling approach. 54 resources of each repository were chosen to form the sample. The systematic observation was utilized to collect data. Data were analyzed descriptively by using Chi-square test. FINDINGS: More than 60% of the resources in the repositories of Iranian universities of medical sciences are full text, and there is a significant difference in access to the full text of the resources among the repositories. The results demonstrated that the indexing of English resources is higher than that of Persian resources in Google search engine, while there is not a significant difference between English and Persian resources in terms of indexing. However, there is a significant difference between English and Persian resources in terms of their accessibility. There is a significant difference between the repositories of Iranian universities of medical sciences in terms of their accessibility in Google. CONCLUSIONS: The results revealed that the indexing of resources in Google does not guarantee their accessibility in it. In terms of the availability of resources in Google, the repository of Army of the Islamic Republic of Iran ranked first, that of Qazvin University of Medical Sciences ranked second, and the repositories of Ardabil University of Medical Sciences and Bushehr University of Medical Sciences ranked third. However, in terms of accessibility, the repository of Qazvin University of Medical Sciences ranked first and the repository of Army of the Islamic Republic of Iran ranked second and that of Ardabil University of Medical Sciences ranked third.

Considering the growth of digital marketing, the present study was conducted by webometrics approach. In this study Iranian university library websites are analyzed from the standpoint of Google search engine optimization (SEO). Moreover, identifying the effective factors of Google SEO is done. The number of 30 effective components on Google SEO was obtained by using the agreement between two intelligent online SEO analysis tools ‘SEOsitecheckup’ and ‘SEOPtimer’, with a reliability coefficient of 0. 77. The number of 17 components were identified with the help of texts and the library method (the numbers of 12 components were common in both methods). The research community consisted of 42 library websites of Iranian universities affiliated with the Ministry of Science, Research and Technology located in major cities of Iran. The data collection tools were the online analysis databases ‘SEOptimer’, ‘Ahrefs’, ‘Similarweb’, and ‘W3 Consortium’ (in addition to the checklist and identified components). The members of the community were analyzed from the standpoint of the 34 measurable components by SEO analysis tools (no need for the manager or administrator of the same website) on the cross-sectional study for six months at the beginning of the year 2020). ‘Excel’ and ‘LibreOffice Click’ software were used to analyze the data. None of the websites of Iran's university libraries got the final SEO score of 75 out of 100. Only thirty-three percent of Iranian university library websites had observed the ‘digital security certificate’ effective component on SEO. The websites of the central library of Sharif university of technology, and the digital library of Shahid Beheshti university were ranked respectively, from standpoint of the final Google SEO score. Connecting the website to various social networks such as LinkedIn, Facebook, and Instagram, is necessary to improve Google's SEO score, but it is not sufficient (only one effective component). The components affecting Google SEO can be divided into two groups. The first category can be adjusted by the administrators of the library website (including 29 components of the checklist offered in this present study and 1 component of the library website's compliance with the rules of the W3 consortium). The second category (including 4 components: ‘bounce rate’, ‘average visit duration’, ‘number of pages indexed in Google’, and ‘number of clicks or visits of uses) can be measured by the feedback of other websites or clicking of the users. Improving SEO-effective components along with having rich content will be valuable together with each other.