Post-Traumatic Growth and Identity Construction in Covid-19 Messages Delivered to the People of Ghana by President Nana Akuffo Addo

This work analyses post-traumatic growth in Covid-19 addresses delivered to the people of Ghana by President Nana Akuffo Addo. We draw on Post-Traumatic Growth Theory to explain how Akuffo Addo constructs a new identity for himself and the nation in order to navigate through the pandemic and forge an agenda of growth and prosperity for Ghana. The study employs a linguistic content analysis approach. The data consists of twenty different speeches from the president to the people. The speeches are first analysed and coded manually for the five main tenets of Post-Traumatic Growth (PTG) identified in the updates. Consequently, the linguistic markers that are used in reconstructing the Ghanaian identity in response to the pandemic are delineated and mapped to the goals of the president using the Linguistic Inquiry and Word Count 2015 (LIWC2015; Pennebaker et al., 2015) software; a vocabulary analysis tool. The analysis showed that there was a high prevalence of personal pronoun use, use of positive-emotion words, and cognitive-processing words. This confirms our hypothesis that linguistic markers can be used to detect PTG.

PROCESSING EMOTION WORDS IN THE LATE-LEARNED L2

Language-emotion link has been a subject of interest for several decades. It has been studied extensively both in the monolingual and bilingual literature. However, due to the numerous factors that are at play in bilingualism, i.e. age and context of acquisition, frequency of use, there is conflicting evidence regarding the emotional load of each language of bilinguals. A great bulk of evidence leans towards the L1 as the more emotional language. This study investigates the perceived emotionality in the late learned language. Our participants (N = 57) were late bilinguals who learned their second language (English) in formal contexts after their first language (Turkish). We used a lexical decision task in which the participants determined whether the visually presented emotion words were real words or non-words. In line with the literature, we report faster response times for positive than for negative words in both languages. Also, the results showed L1 superiority in word processing.

Emotion Valence for Social Communication During Story Retells

Purpose: Children's narratives may differ based on whether they are describing events that elicit positive versus negative emotions and may be more detailed when talking about negative emotions. Understanding how children retell stories representing varied emotions may guide educators in providing opportunities for children to develop social communication. This study examined retells of stories depicting positive versus negative emotions and responses to follow-up questions relating to facets of social communication. Method: Video stories depicting positive versus negative emotions were presented to 22 preschool children (ages 4;1–5;3 [years;months]). Macrostructure in the retells (measured by the Index of Narrative Complexity) and talk about emotions (measured by number and variety of emotion words) and action/attempts (rated by a rubric for quality of response) were analyzed. Results: The only significant result was the difference between the number of times the macro element, complication, was included in retells, with a greater number in the negative condition. Conclusion: The consistent quality of retells across emotion valence suggests that positive and negative emotions may both be used in fictional stories depicting social scenarios to develop opportunities to assess and talk about facets of social communication.

Cognitive networks identify dimensions of distress in suicide notes: Anxiety, emotional profiles, and "words not said"

Suicide remains a serious public-health concern that is difficult to accurately predict in real-world settings. To identify potential predictors of suicide, we examined the emotional content of suicide notes using methods from cognitive network science. Specifically, we compared the co-occurrence networks of suicide notes with those constructed out of emotion words written by individuals scoring low or high on measures of depression, anxiety, and stress. Our objective was to identify which networks were most similar to the suicide notes network, in particular with regard to the connectivity between words and their emotional contents. We also investigated what types of words remained in the high/low emotion networks after controlling for the words present in the suicide notes, which we conceptualize as the “words not said” in the suicide notes. We found that patterns of connectivity among emotion words in suicide notes were most similar to those in texts written by low-anxiety individuals. However, upon analyzing the “words not said” in suicide notes, we observed that the remaining collection of emotions in suicide notes was most similar to those expressed by high-anxiety individuals. We discuss how these findings relate with existing clinical psychological literature as well as their potential implications for predicting suicidal behavior.

Linguistic analysis of online domestic violence testimonies in the context of COVID-19

Worldwide, an increase in cases and severity of domestic violence (DV) has been reported as a result of the 2019 Coronavirus Disease (COVID-19) pandemic. As one’s language can provide insight in one’s mental health, this study analyzed word use in a DV online support group, aiming to investigate the impact of the COVID-19 pandemic on DV victims. Words reflecting social support and leisure activities were investigated as protective factors against linguistic indicators of depression. 5856 posts were collected from the r/domesticviolence subreddit and two neutral comparison subreddits (r/changemyview & r/femalefashionadvice). In the DV support group, the average number of daily posts increased significantly by 22% from pre-pandemic to mid-pandemic. Confirmatory analysis was conducted following a registered pre-analysis plan. DV victims used significantly more linguistic indicators of depression than individuals in the comparison groups. These linguistic indicators did not change with the onset of COVID-19. The use of negative emotion words was negatively related to the use of social support words (Spearman’s rho correlation coefficient [rho] = -.110) and words referring to leisure activities (rho = -.137). Pre-occupation with COVID-19 was associated with the use of negative emotion words (rho = .148).We conclude that language of DV victims is characterized by indicators of depression and this characteristic is stable over time. Concerns with COVID-19 could contribute to negative emotions, whereas social support and leisure activities could function to some degree as protective factors. A potential weakness of this study could be the limited ability of word count methods to assess the impact of stressors such as COVID-19. Future studies could make use of natural language processing and other advanced methods of linguistic analysis to learn about the mental health of DV victims.

Linguistic analysis of online domestic violence testimonies in the context of COVID-19

Worldwide, an increase in cases and severity of domestic violence (DV) has been reported as a result of the 2019 Coronavirus Disease (COVID-19) pandemic. As one’s language can provide insight in one’s mental health, this study analyzed word use in a DV online support group, aiming to investigate the impact of the COVID-19 pandemic on DV victims. Words reflecting social support and leisure activities were investigated as protective factors against linguistic indicators of depression. 5856 posts were collected from the r/domesticviolence subreddit and two neutral comparison subreddits (r/changemyview & r/femalefashionadvice). In the DV support group, the average number of daily posts increased significantly by 22% from pre-pandemic to mid-pandemic. Confirmatory analysis was conducted following a registered pre-analysis plan. DV victims used significantly more linguistic indicators of depression than individuals in the comparison groups. These linguistic indicators did not change with the onset of COVID-19. The use of negative emotion words was negatively related to the use of social support words (Spearman’s rho correlation coefficient [rho] = -.110) and words referring to leisure activities (rho = -.137). Pre-occupation with COVID-19 was associated with the use of negative emotion words (rho = .148).We conclude that language of DV victims is characterized by indicators of depression and this characteristic is stable over time. Concerns with COVID-19 could contribute to negative emotions, whereas social support and leisure activities could function to some degree as protective factors. A potential weakness of this study could be the limited ability of word count methods to assess the impact of stressors such as COVID-19. Future studies could make use of natural language processing and other advanced methods of linguistic analysis to learn about the mental health of DV victims.

Japanese conservative messages propagate to moderate users better than their liberal counterparts on Twitter

To examine conservative–liberal differences in the extent to which partisan tweets reach less partisan moderate users in a nonwestern context, we analyzed a network of retweets about former Japanese Prime Minister Shinzo Abe. The analyses consistently demonstrated that partisan tweets originating from the conservative cluster reach a wider range of moderate users than those from the liberal cluster. Network analyses revealed that while the conservative and the liberal clusters’ internal structures were similar, the conservative cluster reciprocated the follows from moderate accounts at a higher rate than the liberal cluster. In addition, moderate accounts reciprocated the conservative cluster’s following at a higher rate than they did for the liberal cluster. The analysis of tweet content showed no difference in the frequency of hashtag use between conservatives and liberals, but there were differences in the use of emotion words and linguistic expressions. In particular, emotion words related to the propagation of messages, such as those expressing “dislike”, were used more frequently by conservatives, while the use of adjectives by conservatives was closer to that of moderate users, indicating that conservative tweets are more palatable for moderate users than liberal tweets.