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  • × author_ss:"Spina, D."
  1. Zubiaga, A.; Spina, D.; Martínez, R.; Fresno, V.: Real-time classification of Twitter trends (2015) 0.01 
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    Abstract
    In this work, we explore the types of triggers that spark trends on Twitter, introducing a typology with the following 4 types: news, ongoing events, memes, and commemoratives. While previous research has analyzed trending topics over the long term, we look at the earliest tweets that produce a trend, with the aim of categorizing trends early on. This allows us to provide a filtered subset of trends to end users. We experiment with a set of straightforward language-independent features based on the social spread of trends and categorize them using the typology. Our method provides an efficient way to accurately categorize trending topics without need of external data, enabling news organizations to discover breaking news in real-time, or to quickly identify viral memes that might inform marketing decisions, among others. The analysis of social features also reveals patterns associated with each type of trend, such as tweets about ongoing events being shorter as many were likely sent from mobile devices, or memes having more retweets originating from a few trend-setters.
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.3, S.462-473
    Type
    a
  2. Spina, D.; Trippas, J.R.; Cavedon, L.; Sanderson, M.: Extracting audio summaries to support effective spoken document search (2017) 0.01 
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    Abstract
    We address the challenge of extracting query biased audio summaries from podcasts to support users in making relevance decisions in spoken document search via an audio-only communication channel. We performed a crowdsourced experiment that demonstrates that transcripts of spoken documents created using Automated Speech Recognition (ASR), even with significant errors, are effective sources of document summaries or "snippets" for supporting users in making relevance judgments against a query. In particular, the results show that summaries generated from ASR transcripts are comparable, in utility and user-judged preference, to spoken summaries generated from error-free manual transcripts of the same collection. We also observed that content-based audio summaries are at least as preferred as synthesized summaries obtained from manually curated metadata, such as title and description. We describe a methodology for constructing a new test collection, which we have made publicly available.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.9, S.2101-2115
    Type
    a

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