5 resultados para Tweet contextualization

em QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast


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David Norbrook, Review of English Studies 56 (Sept. 2005), 675-6.
‘We have waited a long time for a study of Marvell’s Latin poetry; fortunately, Estelle Haan’s monograph generously makes good the loss ... One of her most intriguing suggestions … is that Marvell may have presented paired poems like ‘Ros’ and ‘On a Drop of Dew’, and the poems to the obligingly named Dr Witty, to his student Maria Fairfax as his own patterns for the pedagogical practice of double translation. Perhaps the most original parts of the book, however, move beyond the familiar canon to cover the generic range of the Latin verse. Haan offers a very full contextualization of the early Horatian Ode to Charles I in seventeenth-century exercises in parodia. In a rewarding reading of the poem to Dr Ingelo she shows how Marvell deploys the language of Ovid’s Tristia to present Sweden as a place of shivering exile, only to subvert this model with a neo-Virgilian celebration of Christina as a virtuous, city-building Dido. She draws extensively on historical as well as literary sources to offer very detailed contextualizations of the poem to Maniban and ‘Scaevola Scotto-Britannus’... This monograph opens up many new ways into the Latin verse, not least because it is rounded off with new texts and prose translations of the Latin poems. These make a substantial contribution in their own right. They are the best and most accurate translations to date (those in Smith’s edition having some lapses); they avoid poeticisms but bring out the structure of the poems' wordplay very clearly. This book brings us a lot closer to seeing Marvell whole.'

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This integrative review presents a novel hypothesis as a basis for integrating two evolutionary viewpoints on the origins of human cognition and communication, the sexual selection of human mental capacities, and the social brain hypothesis. This new account suggests that mind-reading social skills increased reproductive success and consequently became targets for sexual selection. The hypothesis proposes that human communication has three purposes: displaying mind-reading abilities, aligning and maintaining representational parity between individuals to enable displays, and the exchange of propositional information. Intelligence, creativity, language, and humor are mental fitness indicators that signal an individual’s quality to potential mates, rivals, and allies. Five features central to the proposed display mechanism unify these indicators, the relational combination of concepts, large conceptual knowledge networks, processing speed, contextualization, and receiver knowledge. Sufficient between-mind alignment of conceptual networks allows displays based upon within-mind conceptual mappings. Creative displays communicate previously unnoticed relational connections and novel conceptual combinations demonstrating an ability to read a receiver’s mind. Displays are costly signals of mate quality with costs incurred in the developmental production of the neural apparatus required to engage in complex displays and opportunity costs incurred through time spent acquiring cultural knowledge. Displays that are fast, novel, spontaneous, contextual, topical, and relevant are hard-to-fake for lower quality individuals. Successful displays result in elevated social status and increased mating options. The review addresses literatures on costly signaling, sexual selection, mental fitness indicators, and the social brain hypothesis; drawing implications for nonverbal and verbal communication.

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When a user of a microblogging site authors a microblog
post or browses through a microblog post, it provides cues as to what
topic she is interested in at that point in time. Example-based search
that retrieves similar tweets given one exemplary tweet, such as the one
just authored, can help provide the user with relevant content. We investigate
various components of microblog posts, such as the associated
timestamp, author’s social network, and the content of the post, and
develop approaches that harness such factors in finding relevant tweets
given a query tweet. An empirical analysis of such techniques on real
world twitter-data is then presented to quantify the utility of the various
factors in assessing tweet relevance. We observe that content-wise similar
tweets that also contain extra information not already present in the
query, are perceived as useful. We then develop a composite technique
that combines the various approaches by scoring tweets using a dynamic
query-specific linear combination of separate techniques. An empirical
evaluation establishes the effectiveness of the composite technique, and
that it outperforms each of its constituents.

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Analysing public sentiment about future events, such as demonstration or parades, may provide valuable information while estimating the level of disruption and disorder during these events. Social media, such as Twitter or Facebook, provides views and opinions of users related to any public topics. Consequently, sentiment analysis of social media content may be of interest to different public sector organisations, especially in the security and law enforcement sector. In this paper we present a lexicon-based approach to sentiment analysis of Twitter content. The algorithm performs normalisation of the sentiment in an effort to provide intensity of the sentiment rather than positive/negative label. Following this, we evaluate an evidence-based combining function that supports the classification process in cases when positive and negative words co-occur in a tweet. Finally, we illustrate a case study examining the relation between sentiment of twitter posts related to English Defence League and the level of disorder during the EDL related events.

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With Tweet volumes reaching 500 million a day, sampling is inevitable for any application using Twitter data. Realizing this, data providers such as Twitter, Gnip and Boardreader license sampled data streams priced in accordance with the sample size. Big Data applications working with sampled data would be interested in working with a large enough sample that is representative of the universal dataset. Previous work focusing on the representativeness issue has considered ensuring the global occurrence rates of key terms, be reliably estimated from the sample. Present technology allows sample size estimation in accordance with probabilistic bounds on occurrence rates for the case of uniform random sampling. In this paper, we consider the problem of further improving sample size estimates by leveraging stratification in Twitter data. We analyze our estimates through an extensive study using simulations and real-world data, establishing the superiority of our method over uniform random sampling. Our work provides the technical know-how for data providers to expand their portfolio to include stratified sampled datasets, whereas applications are benefited by being able to monitor more topics/events at the same data and computing cost.