Language reflects several cognitive variables that are grounded in cognitive linguistics, psycholinguistics and sociolinguistics. This paper examines how Arab populations reacted to the COVID-19 pandemic on Twitter over twelve weeks since the outbreak. We conducted a lexicon-based thematic analysis using corpus tools, and LIWC and applied R language's stylo. The dominant themes that were closely related to coronavirus tweets included the outbreak of the pandemic, metaphysics responses, signs and symptoms in confirmed cases, and conspiracism. The psycholinguistic analysis also showed that tweeters maintained high levels of affective talk, which was loaded with negative emotions and sadness. Also, LIWC's psychological categories of religion and health dominated the Arabic tweets discussing the pandemic situation. In addition, the contaminated counties that captured most of the attention of Arabic tweeters were China, the USA, Italy, Germany, India, and Japan. At the same time, China and the USA were instrumental in evoking conspiracist ideation about spreading COVID-19 to the world.

Because language represents advanced aspects of human cognition, studying linguistic styles and figurative meaning have proven effective in measuring embodied cognition about the external world. This article defines the most worrisome topics people discussed from Weeks 1 to 14 after the outbreak and compares the message delivered by the literal use of words to the figurative use of metaphoric expressions. We bootstrapped representative data from Twitter over 14 weeks since the inception of the outbreak to be analyzed thematically using the Linguistic Inquiry and Word Count (LIWC) 2015 software as well as corpus tools. The MetaNet database, corpus tools, and manual annotation were used to detect expressions that can be linguistically mapped to the formalized list of conceptual metaphors. The most frequently tagged themes included ‘the outbreak of the pandemic, its epidemiology, its prophylaxis measures, national and world economies, media’, as well as the ‘signs and symptoms of COVID-19’. Although LIWC-based analysis showed English-speaking tweeters maintained high levels of analytical thinking, elevated levels of anger, anxiety, and doubtfulness, there were discrepancies and improper conceptualization of the clinical picture of the pandemic.

For years, the Internet has provided patients with mental health disorders with several platforms where they share their personal experiences with their medical conditions. This study aims at exploring online narratives shared by patients with Bipolar Depression disorder where they self-report their medical diagnoses of the disorder and reflect on the hardships they go through in their lives. The study employs Martin and White's (2005) Appraisal Theory to examine the JUDGMENTS that patients make about their behaviors and the behaviors of people around them. In order to extract the JUDGMENT utterances from the corpus of narratives, the study uses syntactic patterns that may yield evaluative utterances. The results of the study show that judgments which belong to capacity [i.e., how (in)capable a person is] and propriety [i.e., how (un)ethical a person is] measure the highest scores among all other subtypes of JUDGMENT. The study also provides a lexicon for the most frequent expressions that convey JUDGMENT, which could be used to enrich the Appraisal resources.