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    <titleInfo>
        <title>Winter is here: Summarizing Twitter Streams related to Pre-Scheduled Events</title>
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    <name type="personal">
        <namePart type="given">Anietie</namePart>
        <namePart type="family">Andy</namePart>
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    <name type="personal">
        <namePart type="given">Derry</namePart>
        <namePart type="given">Tanti</namePart>
        <namePart type="family">Wijaya</namePart>
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    <name type="personal">
        <namePart type="given">Chris</namePart>
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        <dateIssued>2019-08</dateIssued>
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            <title>Proceedings of the Second Workshop on Storytelling</title>
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            <namePart type="given">Francis</namePart>
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        <name type="personal">
            <namePart type="given">Ting-Hao</namePart>
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            <namePart type="family">Huang</namePart>
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            <namePart type="given">Stephanie</namePart>
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            <namePart type="given">Margaret</namePart>
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    <abstract>Pre-scheduled events, such as TV shows and sports games, usually garner considerable attention from the public. Twitter captures large volumes of discussions and messages related to these events, in real-time. Twitter streams related to pre-scheduled events are characterized by the following: (1) spikes in the volume of published tweets reflect the highlights of the event and (2) some of the published tweets make reference to the characters involved in the event, in the context in which they are currently portrayed in a subevent. In this paper, we take advantage of these characteristics to identify the highlights of pre-scheduled events from tweet streams and we demonstrate a method to summarize these highlights. We evaluate our algorithm on tweets collected around 2 episodes of a popular TV show, Game of Thrones, Season 7.</abstract>
    <identifier type="citekey">andy-etal-2019-winter</identifier>
    <identifier type="doi">10.18653/v1/W19-3412</identifier>
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        <date>2019-08</date>
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%0 Conference Proceedings
%T Winter is here: Summarizing Twitter Streams related to Pre-Scheduled Events
%A Andy, Anietie
%A Wijaya, Derry Tanti
%A Callison-Burch, Chris
%Y Ferraro, Francis
%Y Huang, Ting-Hao ‘Kenneth’
%Y Lukin, Stephanie M.
%Y Mitchell, Margaret
%S Proceedings of the Second Workshop on Storytelling
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F andy-etal-2019-winter
%X Pre-scheduled events, such as TV shows and sports games, usually garner considerable attention from the public. Twitter captures large volumes of discussions and messages related to these events, in real-time. Twitter streams related to pre-scheduled events are characterized by the following: (1) spikes in the volume of published tweets reflect the highlights of the event and (2) some of the published tweets make reference to the characters involved in the event, in the context in which they are currently portrayed in a subevent. In this paper, we take advantage of these characteristics to identify the highlights of pre-scheduled events from tweet streams and we demonstrate a method to summarize these highlights. We evaluate our algorithm on tweets collected around 2 episodes of a popular TV show, Game of Thrones, Season 7.
%R 10.18653/v1/W19-3412
%U https://aclanthology.org/W19-3412
%U https://doi.org/10.18653/v1/W19-3412
%P 112-116

Markdown (Informal)

[Winter is here: Summarizing Twitter Streams related to Pre-Scheduled Events](https://aclanthology.org/W19-3412) (Andy et al., Story-NLP 2019)

• Winter is here: Summarizing Twitter Streams related to Pre-Scheduled Events (Andy et al., Story-NLP 2019)

ACL

• Anietie Andy, Derry Tanti Wijaya, and Chris Callison-Burch. 2019. Winter is here: Summarizing Twitter Streams related to Pre-Scheduled Events. In Proceedings of the Second Workshop on Storytelling, pages 112–116, Florence, Italy. Association for Computational Linguistics.