Abstract
Nowadays, social media has swiftly altered the media landscape resulting in a competitive environment of news creation and dissemination. Sharing news through social media websites is almost provided in a textual format. The nature of the disseminated text is considered as unstructured text. Text mining techniques play a significant role in transforming the unstructured text into informative knowledge with various interesting patterns. Due to the lack of literature on textual analysis of news channels’ in social media, the current study seeks to explore this genre of new media discourse through analyzing news channels online textual data and transforming its quantifiable information into constructive knowledge. Accordingly, this study applies various text mining techniques on this under-researched context aiming at extracting knowledge from unstructured textual data. To this end, three news channels have been selected, namely Fox News, CNN, and ABC News. Data has been collected from the Facebook pages of these three news channels through Facepager tool which was then processed using RapidMiner tool. Findings indicated that USA elections news received the highest coverage among others in these channels. Moreover, results revealed that the most frequent shared posts regarding the USA elections were tackled by the CNN followed by ABC News, and Fox News, respectively. Additionally, results revealed a significant relationship between ABC News and CNN in covering similar topics.
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