Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Detecting Real-World Influence Through Twitter   In this paper, we investigate the issue of detecting the real-life influence
of people based on their Twitter account. We propose an overview of common
Twitter features used to characterize such accounts and their activity, and
show that these are inefficient in this context. In particular, retweets and
followers numbers, and Klout score are not relevant to our analysis. We thus
propose several Machine Learning approaches based on Natural Language
Processing and Social Network Analysis to label Twitter users as Influencers or
not. We also rank them according to a predicted influence level. Our proposals
are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art
ranking methods.

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The categories of this paper are: eess
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
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The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom
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The categories of this paper are: q-fin
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The categories of this paper are: stat
3
The categories of this paper are: physics
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The categories of this paper are: dg-ga
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The categories of this paper are: patt-sol
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The categories of this paper are: math
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The categories of this paper are: funct-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: atom-ph