A multi-dimensional analysis of the representation of conference interpreters in the Chinese media

Though interpreters’ professionalism has been discussed in interpreting studies, there have been few studies on how the general public see the image of interpreters. The present study is a multi-dimensional analysis of the image of conference interpreters as represented by the media, which is based on a corpus of 60 news reports about interpreting and interpreters in the Chinese media in the past 10 years. It explores the research question: How are conference interpreters represented in the Chinese media? Through thematic and rhetorical analysis of the headlines and body texts as well as multimodal analysis of the photos in the news reports, it is found that conference interpreters are represented by institutional conference and diplomatic interpreters, who are in turn represented as “stars” or public celebrities of the profession; they are frequently presented along with big events and big names, and portrayed as affiliating to power and as distant from the public. Images of female beauties among them are also selected and “consumed” as in popular culture. This implies a discrepancy between the self-perception of the interpreting profession and their representation by the media.

Everyday Space Weather

This paper is a reflection on some examples of how human civilization today experiences space weather as a daily phenomenon. Most of the scientific discussion of space weather so far has been dominated by talk of big events or extreme space weather. This summarizes space weather as something ordinary people grapple with, enjoy, and pay for on a daily basis—no “super storms” required. The time has come to start discussing space weather as if it is an everyday occurrence. Because it is.

Monitoring Depression Trend on Twitter during the COVID-19 Pandemic: Observational Study (Preprint)

BACKGROUND The COVID-19 pandemic has severely affected people’s daily lives and caused tremendous economic loss worldwide. Anecdotal evidence suggests that the pandemic has increased the depression level among the population. However, systematic studies of depression detection and monitoring during the depression are lacking. OBJECTIVE This study aims (1) to develop a method to accurately identify people with depression by analyzing their tweets and (2) to monitor the population-wise depression level on Twitter. METHODS To study this subject, we design an effective regular expression-based search method and create by far the largest English Twitter depression dataset containing 2,575 distinct identified depression users (N=2,575) with their past tweets. To examine the effect of depression on people’s Twitter language, we train three transformer-based depression classification models on the dataset, evaluate their performance with progressively increased training sizes, and compare the model’s “tweet chunk”-level and user-level performances. Furthermore, inspired by psychological studies, we create a fusion classifier that combines deep learning model scores with psychological text features and users’ demographic information and investigate these features’ relations to depression signals. Finally, we demonstrate our model’s capability of monitoring both group-level and population-level depression trends by presenting two of its applications during the COVID-19 pandemic. RESULTS Our fusion model demonstrates an accuracy of 78.9% on a test set containing 446 people (N=446), half of which are identified as suffering from depression. Conscientiousness, neuroticism, appearance of first-person pronouns, talking about biological processes such as eat and sleep, talking about power, and exhibiting sadness are shown to be important features in depression classification. Further, when used for monitoring the depression trend, our model shows that depressive users, in general, respond to the pandemic later than the control group based on their tweets. It is also shown that three states of the United States - New York (NY), California (CA), and Florida (FL) - share a similar depression trend as the whole US population. When compared to NY and CA, people in FL demonstrate a significantly lower level of depression. CONCLUSIONS This study proposes an efficient method that can be used to analyze the depression level of different groups of people on Twitter. We hope this study can raise awareness among researchers and the general public of COVID-19’s impact on people’s mental health. The non-invasive monitoring system can also be rapidly adapted to other big events besides COVID-19 and might be useful during future outbreaks.

Every Epoch, Time Frame or Date that Is Solid Melts into Air. Does It? The Entanglements of Global History and World Society

This chapter explores the possibilities of a fruitful exchange between world society theory and global history approaches. It uses turning points in analyzing the quality of the accounts of the exchange and confirms whether these accounts of significant change can be linked to one another. It also mentions the unification of global history and world society theory in rejecting any obvious 'telos' of history. The chapter explains that in global history, the rejection takes the form of a narrative in which history unfolds as nothing but a transformation of complexity, while in world society theory it takes the form of a theory of social evolution. It discusses possible substantive overlaps between global history and world society theory, which focuses on epochal change, the role of the long nineteenth century, and the role of single big events or turning points.