Article citationsMore>>

Zhao, W.X., Jiang, J., Weng, J.S., He, J., Lim, E.-P., Yan, H.F. and Li, X.M. (2011) Comparing Twitter and Traditional Media Using Topic Models. In: Advances in Information Retrieval: 33rd European Conference on IR Research, Dublin, Ireland, 18-21 April 2011, Springer Verlag, Berlin.

has been cited by the following article:

• TITLE: Hashtags as Crowdsourcing: A Case Study of Arabic Hashtags on Twitter

  AUTHORS: Batool Hendal

  KEYWORDS: Hashtag, Twitter, Social Media, Translators, Crowdsourcing, Translation Studies, Twitter Content Classifications

  JOURNAL NAME: Social Networking, Vol.8 No.4, October 30, 2019

  ABSTRACT: This mixed study aims to highlight the impact of social media in the Arab world, specifically Twitter’s impact on translators’ communities. For this purpose, the role of hashtags among translators will be examined by investigating one particular Arabic hashtag, its purpose, target users, and the classification of content. The hashtag is , #translator_serving_translator. 1) An online survey of six closed questions was employed and posted on Twitter, and 249 responses show that users are from fourteen Arab countries, and the majority is from Saudi Arabia. Hashtag users are translators, freelancers, or TS students. Some are active users who post tweets and answer questions, others only ask questions, and the rest only read tweets. The general attitude toward employing hashtags among translators’ communities was positive. 2) Employing a content analysis approach, the content is classified into two main categories of sharing information and seeking assistance with seven subcategories of each.