Short abstract:

Our project explores collaborating with algorithms to analyse emotionally charged social media discussions. By embracing a 'transmedial storytelling' approach, we unveiled both big data patterns and nuanced micropolitics within social media practices.

Long abstract:

This abstract offers an innovative approach to working with algorithms in the space between distant and close views of data. For doing so, we will present a project that explored affectively charged social media exchanges about remote schooling in Hungary during the pandemic. Through a cooperation with SentiOneTM, an AI-based social listening tool, we adopted a style of digital research that Blackman (2019) calls ‘transmedial storytelling’: an ethnographic approach for working with digital archives.

Although SentiOneTM offers computational means for data analysis, including commercial sentiment analysis, it was not compatible with our philosophies of affect and emotion. We started with the initial data provided by SentiOneTM‘ but added ‘a human in the loop’ and built an iterative approach into the research design. This included a preliminary reading of media articles related to remote learning during Covid-19 to pinpoint keywords. These were later paired with emotion words to form an algorithm that would crawl Facebook for the desired media articles and social media responses.

Attuning to affect we selected three ‘data events’ that could be curated into a data-story. They included a variety of digital data including comments, emoji and images. By employing a multimodal analytic that analysed the social actors, visual images, non-linguistic reactions and broader social-economic-political relations, we were able to look at big data patterns but also the micropolitics embodied and embedded in everyday (social media) practices.