Together with Adina Nerghes (KNAW), Jay Lee received the best methods paper award at the annual Social Media and Society conference held in Toronto, Canada in July, 2017.
The paper, titled "Labels and sentiment in social media: On the role of perceived agency in online discussion of the refugee crisis", explored the themes and sentiments surrounding online (YouTube) discussion of the refugee/migrant crisis. Specifically, several digital research methods (computer-aided text analysis, topic modeling, sentiment analysis, and semantic network analysis) were jointly employed to uncover the changing sentiments hence framing of key labels employed by commenters in the online discussions of the crisis.
The findings reveal that perception of agency influences the sentiments in comments, with labels associated with higher agency or more freedom (such as 'migrant' or 'immigrant') exhibiting increasingly negative sentiment over the course of a year. Those labels associated with perceived constraint (e.g., 'Syrian') were more stable and positively discussed. Furthermore, discussion of positively related themes was more coherent and partitioned, whereas negative themes overlapped in the terms used and were less distinctive.
"The use of labels to frame these recent events in Europe can have implications for the lives and safety of refugees, they can undermine public support, steer public opinion, and influence reactions to this crisis."