One of the most challenging and difficult aspects of translation is related to the translation of idioms by both machine systems and human translators. The problem arises from the recognition of the idioms on the one hand and translating them on the other hand. idiom recognition is not easy since an idiom may change its form based on the context in which it is used. Using systematic idiom collections and special rules to recognize idioms, the problem can be solved to a great extent. Moreover, the inclusion of a morphological as well as syntactic parser can greatly enhance the idiom recognition and translation process. The aim of this paper is twofold. First, we want to test Google Translate to see how well it is capable of translating idioms and next to provide means to improve the quality of translation of idioms by Google Translate.

The memetic mapping is a method to identify memes and categorize them in the form of a multilevel model. Meme is a reproducible cultural element and has been adapted from the concept of gene in the evolutionary biology. The purpose of this study was to describe the current culture of Google using memetic mapping. To this end, the printed and online texts published about Google were coded for memes. This way, 134 memes were extracted and reduced to 40 Memecules (a higher unit comprised of several related memes). Memecules were placed into 6 levels of organizational culture, namely fundamental assumptions, values, norms, artifacts, work procedures, and human resources procedures. Then, in order to understand the formation of Google’ s culture in the course of history, cultural genealogy method was applied. In this approach, the father of each meme (the culture from which the meme is adopted) is identified, and then the children are connected to their parents with an arrow. The outcome is a cultural genealogy model in shape of a family tree graph. In this study, 85 Greek and Indian cultures with impacts on the emergence of Google’ s culture were recognized and analyzed. This study successfully operationalized the meme concept and employed it in an empirical research. Memetic mapping is a powerful tool available for managers, consultants, and organizational researchers to explore the cultural phenomenon and to change it. Memetic engineering is capable of diagnosing and correcting a defective meme in a human community.

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.