Mining Effective Strategies for Climate Change Communication (Papers Track)

Aswin Suresh (EPFL); Lazar Milikic (EPFL); Francis Murray (EPFL); Yurui Zhu (EPFL); Matthias Grossglauser (École Polytechnique Fédérale de Lausanne (EPFL))

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Natural Language Processing Behavioral and Social Science Public Policy Societal Adaptation & Resilience Data Mining Interpretable ML Unsupervised & Semi-Supervised Learning


With the goal of understanding effective strategies to communicate about climate change, we build interpretable models to rank tweets related to climate change with respect to the engagement they generate. Our models are based on the Bradley-Terry model of pairwise comparison outcomes and use a combination of the tweets’ topic and metadata features to do the ranking. To remove confounding factors related to author popularity and minimise noise, they are trained on pairs of tweets that are from the same author and around the same time period and have a sufficiently large difference in engagement. The models achieve good accuracy on a held-out set of pairs. We show that we can interpret the parameters of the trained model to identify the topic and metadata features that contribute to high engagement. Among other observations, we see that topics related to climate projections, human cost and deaths tend to have low engagement while those related to mitigation and adaptation strategies have high engagement. We hope the insights gained from this study will help craft effective climate communication to promote engagement, thereby lending strength to efforts to tackle climate change.