The increasing prevalence of technology in society has an impact on young people’s language use and development. Greeklish is the writing of Greek texts using the Latin instead of the Greek alphabet, a practice known...The increasing prevalence of technology in society has an impact on young people’s language use and development. Greeklish is the writing of Greek texts using the Latin instead of the Greek alphabet, a practice known as Latinization, also employed for many non-latin alphabet languages. The primary aim of this research is to evaluate the effect of Greeklish on reading time. A sample of 732 young Greeks were asked about their habits when communicating through e-mail and social media with their friends and they then participated in an experiment in which they were asked to read and understand two short texts, one written in Greek and the other in Greeklish. The findings of the research show that nearly one third of the participants use Greeklish. The results of the experiment conducted reveal that understanding is not affected by the alphabet used but reading Greeklish is significantly more time consuming than reading Greek independently of the sex and the familiarity of the participants with Greeklish. The findings suggest that amending social and communication media with software utilities related to Latinization such as language identifiers and converters may reduce reading time and thus facilitate written communication among the users.展开更多
In this paper we deal with Twitter and the presence of the keyword “Macedonia” in tweets over a period of time. We searched for the same term in three different languages, i.e. “Μακεδονíα”, “Macedoni...In this paper we deal with Twitter and the presence of the keyword “Macedonia” in tweets over a period of time. We searched for the same term in three different languages, i.e. “Μακεδονíα”, “Macedonia” and “Македонска - Македониjа”, since we are primarily interested in views from Greece and FYROM without excluding views from other regions. We use methods from Social Network Analysis (SNA) in order to create networks of users, calculate some main network metrics, measure user importance and investigate the presence of possible fragmentations—communities among them. We furthermore proceed to a form of content analysis, using pairs of words within tweets, in order to obtain main ideas, trends and public views that circulated over the network.展开更多
文摘The increasing prevalence of technology in society has an impact on young people’s language use and development. Greeklish is the writing of Greek texts using the Latin instead of the Greek alphabet, a practice known as Latinization, also employed for many non-latin alphabet languages. The primary aim of this research is to evaluate the effect of Greeklish on reading time. A sample of 732 young Greeks were asked about their habits when communicating through e-mail and social media with their friends and they then participated in an experiment in which they were asked to read and understand two short texts, one written in Greek and the other in Greeklish. The findings of the research show that nearly one third of the participants use Greeklish. The results of the experiment conducted reveal that understanding is not affected by the alphabet used but reading Greeklish is significantly more time consuming than reading Greek independently of the sex and the familiarity of the participants with Greeklish. The findings suggest that amending social and communication media with software utilities related to Latinization such as language identifiers and converters may reduce reading time and thus facilitate written communication among the users.
文摘In this paper we deal with Twitter and the presence of the keyword “Macedonia” in tweets over a period of time. We searched for the same term in three different languages, i.e. “Μακεδονíα”, “Macedonia” and “Македонска - Македониjа”, since we are primarily interested in views from Greece and FYROM without excluding views from other regions. We use methods from Social Network Analysis (SNA) in order to create networks of users, calculate some main network metrics, measure user importance and investigate the presence of possible fragmentations—communities among them. We furthermore proceed to a form of content analysis, using pairs of words within tweets, in order to obtain main ideas, trends and public views that circulated over the network.
