Rising popularity of social media platforms has led to many online exchanges on emergent topics by citizens globally. The growth in obesity rates worldwide has fuelled ongoing obesity-related discussions over social media. This study investigates the existence of weight stigma targeted towards different genders in online discussions. Using a mixed method analysis approach, we examined sentiments and word co-occurrences associated with weight stigma from the data corpus captured from Twitter and YouTube. Using the objectification theory as the underlying theory to examine the experiential consequences, our study reveals many sentiments over online discourses and reports significant gender-based differences in the stigmatising content, with more intensity in negative emotions targeting female objectification than males. This study bridges data mining and social construction studies with embedded analytics to share new insights on human behaviours that can help extend our understanding of sentiments that lead to male and female objectification.