Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mining Features Associated with Effective Tweets   What tweet features are associated with higher effectiveness in tweets?
Through the mining of 122 million engagements of 2.5 million original tweets,
we present a systematic review of tweet time, entities, composition, and user
account features. We show that the relationship between various features and
tweeting effectiveness is non-linear; for example, tweets that use a few
hashtags have higher effectiveness than using no or too many hashtags. This
research closely relates to various industrial applications that are based on
tweet features, including the analysis of advertising campaigns, the prediction
of user engagement, the extraction of signals for automated trading, etc.

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The categories of this paper are: plasm-ph
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The categories of this paper are: nlin
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-th
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The categories of this paper are: mtrl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ph
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The categories of this paper are: adap-org
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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
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The categories of this paper are: stat
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The categories of this paper are: hep-lat
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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-ex