Unpacking emotion on social media marketing in global and emerging local market contexts with evidence from big data

Global brands' localization efforts are often reflected in their social media marketing practices with varying acculturation degrees on the tone, emotion, and symbols used. The similarities and differences of parent brands and their local versions' uses of emotional content present an emerging field of research that can uncover the applicable lessons for better business. This research employs supervised and unsupervised machine learning methodologies to explore the emotional content of international automobile and technology brands' Twitter messages in global and emerging local market contexts. Along the same lines, an analysis of consumers' reflections presents cues on positive word of mouth by understanding the most prominent structural and textual features. We aim to answer two main research questions: (1) What are the apparent emotions in international automobile and technology brands' social media marketing in global and emerging local market contexts; and (2) How does the emotional content of an international brand's Twitter marketing campaign message affect the dispersion and positive word of mouth in social media in global and emerging local market contexts. We contribute to marketing, machine learning, social media marketing, and emotion literatures. Our findings are valuable for internationalizing and born-global firms to engage emotionally with their global and emerging market consumers in their social media marketing