Clickbait for climate change: comparing emotions in headlines and full-texts and their engagement

ABSTRACT

Anthropogenic climate change remains a polarizing topic. As most social media users share articles solely relying on the headline, this raises the question of how emerging digital media reporting – especially in the headlines – shapes the perception of climate change issues and engages audiences. Guided by the dual-systems emotion model and discrete-emotions model, this study compared emotion words used in headlines versus full text among climate change articles – and their social media engagement, using computational methods. Findings suggested that climate change support headlines were more likely to use fear words while denial headlines were significantly more likely to contain emotion words, negatively-valenced words, as well as words for anger, anticipation, disgust, sadness, and surprise. Regarding the full text, denial articles were more likely to contain emotion words, negatively-valenced words, and many discrete emotions related words than support articles. A denial article’s engagement was predicted by the total number of emotion words contained in its headline, whereas a support article’s engagement was predicted by negatively-valenced words and words for fear used in its headline. Emotions contained in the full text did not predict support and -denial articles’ engagement. Findings provide practical guidance on how to increase the engagement level of climate change articles.

KEYWORDS:

• Climate change
• denial
• emotions
• headlines
• engagement

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Professional Development Funds at Northern Arizona University.

Notes on contributors

Zhan Xu

Zhan Xu is Assistant Professor at the School of Communication and Associate Faculty in the Interdisciplinary Health Ph.D. Program at Northern Arizona University. Her areas of expertise include health communication, crisis and risk communication, and new technologies, with a focus on social media analytics and big data analysis utilizing computational methods. Recent publications have appeared in journals such as Health Communication, Health Education & Behavior, and Internet Research.

Mary Laffidy

Mary Laffidy is a PhD student and teaching assistant at the University of Illinois Urbana-Champaign. She studies Communication with a focus in Health Communication and Campaigning. Her research concentrates on the impact of message framing in public health outreach with a focus on issues related to stigmatization and risk perception.

Lauren Ellis

Lauren Ellis recently graduated with a Master’s degree in Psychological Sciences from Northern Arizona University. Her projects focused on human sexuality, sex research, and LGBTQ+ sexual health through an intersectional feminist lens. She is also interested in research utilizing social media and big data online.