AuthorsManzoor, Emaad A.
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AbstractBroadcasts and timelines are the primary mechanism of information exchange in online social platforms today. Services like Facebook, Twitter and Instagram have enabled ordinary people to reach large audiences spanning cultures and countries, while their massive popularity has created increasingly competitive marketplaces of attention. Timing broadcasts to capture the attention of such geographically diverse audiences has sparked interest from many startups and social marketing gurus. However, formal study is lacking on both the timing and frequency problems. In this thesis, we introduce, motivate and solve the broadcast scheduling problem of specifying the timing and frequency of publishing content to maximise the attention received. We validate and quantify three interacting behavioural phenomena to parametrise social platform users: information overload, bursty circadian rhythms and monotony aversion, which is defined here for the first time. Our analysis of the influence of monotony refutes the common assumption that posts on social network timelines are consumed piecemeal independently. Instead, we reveal that posts are consumed in chunks, which has important consequences for any future work considering human behaviour over social network timelines. Our quantification of monotony aversion is also novel, and has applications to problems in various domains such as recommender list diversification, user satiation and variety-seeking consumer behaviour. Having studied the underlying behavioural phenomena, we link schedules, timelines, attention and behaviour by formalising a timeline information exchange process. Our formulation gives rise to a natural objective function that quantifies the expected collective attention an arrangement of posts on a timeline will receive. We apply this formulation as a case-study on real-data from Twitter, where we estimate behavioural parameters, calculate the attention potential for different scheduling strategies and, using the method of marginal allocation, discover a new scheduling strategy that outperforms popular scheduling heuristics while producing fewer posts.