Abstract
INTRODUCTION: Businesses commonly text mine Twitter data to identify patterns and extract valuable information. However, this method is rarely applied to the animal welfare sector. Here, we describe Twitter conversations regarding dogs during a global pandemic, assess the evolution of sentiment, and examine the dynamics of sector influence. METHODS: Between March and August 2020, we gathered 61,088 unique tweets from the United Kingdom and Republic of Ireland, relating to COVID-19 and dogs. Tweets were assigned to one of four pandemic phases and active accounts were assigned to a sector: Personal (i.e., UK and ROI public), Press (i.e., mass media), State (i.e., Government, Police, and NHS), and Other (i.e., welfare organizations, social enterprises, research organizations, charity, and business). RESULTS: Word frequency and sentiment analysis between phases and sectors were assessed, and cross correlation functions and lagged regressions were used to evaluate sector influence. Topical foci of conversations included: meat trade, separation anxiety and dog theft. Sentiment score remained stable until the last phase where sentiment decreased (F (3, 78, 508) = 44.4, p < 0.001), representing an increased use of negative language. Sentiment differed between the four sectors (F (3, 11, 794) = 52.2, p < 0.001), with Personal and Press accounts presenting the greatest use of negative language. Personal accounts were initially partly influenced by State accounts (R = -0.26; p = 0.05), however this altered to Press accounts by the last phase (R = -0.31; p = 0.02). DISCUSSION: Our findings highlight that whilst Personal accounts may affect sector-specific messaging online, perhaps more importantly: language used, and sentiment expressed by Press, State and Other accounts may influence public perception. This draws attention to the importance of sector responsibility regarding accurate and appropriate messaging, as irresponsible/ill-considered comments or campaigns may impact future human-animal interaction.