| dc.description.abstract | Algorithmic news recommendation on news aggregator platforms increasingly shapes what news perspectives people encounter. However, in an effort to increase diversity and reduce the personalized ‘filter
bubble’ effect, many conflicting viewpoints are often blended into a single feed. This thesis explores how
an agonistic approach to news recommendation might better support users in interpreting diverse and
oppositional perspectives.
We introduce the Agonistic News Topic Interpreter (ANTI), a design concept developed through
a Research through Design methodology. ANTI draws on theories of framing and agonistic pluralism to
structure news diversity more intentionally: through the use of frame personae. Two practical design
strategies are presented: persona profiles, that contextualize the personae and provide representative
articles, and substitution articles, intended to recommend users alternative perspectives on similar topics.
Using the MIND and Media Frame Corpus datasets, we train a frame classifier and model frame
personae for three cases: abortion, immigration, and the Hong Kong protests. The results show that
the personae effectively separate perspectives and that these are nicely contextualized by the persona
profiles, enabling more interpretable diversity. Substitution articles show potential but require further
refinement to match events across articles.
We conclude this thesis by summarizing defining elements of an ANTI and agonistic news recommendation, outlining (agonistic) design guidelines for current news platforms and suggesting future
research opportunities. | |
