The stigma of autism in china: an analysis of newspaper portrayals of autism between 2003 and 2012

Abstract

Autism is a highly stigmatized developmental disability in many societies, and the media are major contributors to such stigma. Presented here is the first systematic analysis of Chinese newspapers’ coverage of autism for stigma-causing content. More specifically, this analysis examines the age of autistic people reported, the image of autistic people, and the use of stigma cues (in terms of peril, mark, and shame) and challenge cues (in terms of personification, hope, and fight) in five leading newspapers in China between 2003 and 2012. It finds that while the reportage of autism increases over time, which might contribute to the public’s heightened awareness of the condition, such reportage is often biased. The most common stereotypes about autism in Chinese newspapers are autistic people as children, as patients, or as savants. The most often-used challenge cues are personification and hope, but their uses significantly decrease in percentage from 2003 to 2012. The most often used stigma cues are peril and mark. The use of the shame cue is relatively less frequent, but it increases significantly over the 10-year period. Theoretically, this article provides an application of stigma communication theory in a non-Western context. Practically, it not only contributes to the current knowledge about media representation of autism in China, but also suggests that it is important for media agencies and health care professionals to promote media guidelines and train health journalists for reporting disability issues in a nonstigmatizing way.

Acknowledgments

We thank Dr. Teresa Thompson and two anonymous reviewers for their thoughtful comments and suggestions on earlier drafts.

Notes

¹ For a detailed discussion of rationales of sampling decisions, refer to Bie and Tang (2015).

² Initially, the longitudinal change in the use of the shame cue was not statistically significant. However, after replacing the value of one outlier with the mean, the regression model became significant.