Difference between revisions of "A Wikipedia-Based Approach to Profiling Activities on Social Media"

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{{Infobox work
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| title = A Wikipedia-Based Approach to Profiling Activities on Social Media
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| date = 2018
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| authors = [[Christian Torrero]]<br />[[Carlo Caprini]]<br />[[Daniele Miorandi]]
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| link = https://dl.acm.org/citation.cfm?id=3174140
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| plink = http://arxiv.org/pdf/1804.02245.pdf
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}}
 
'''A Wikipedia-Based Approach to Profiling Activities on Social Media''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Christian Torrero]], [[Carlo Caprini]] and [[Daniele Miorandi]].
 
'''A Wikipedia-Based Approach to Profiling Activities on Social Media''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Christian Torrero]], [[Carlo Caprini]] and [[Daniele Miorandi]].
  
 
== Overview ==
 
== Overview ==
 
Online user profiling is a very active research field, catalyzing great interest by both scientists and practitioners. In this paper, in particular, authors look at approaches able to mine social media activities of users to create a rich user profile. Authors look at the case in which the profiling is meant to characterize the user's interests along a set of predefined dimensions (that authors refer to as [[categories]]). A conventional way to do so is to use semantic analysis techniques to (i) extract relevant entities from the online conversations of users (ii) mapping said entities to the predefined categories of interest. While entity extraction is a well-understood topic, the mapping part lacks a reference standardized approach. In this paper authors propose using graph navigation techniques on the [[Wikipedia]] tree to achieve such a mapping. A prototypical implementation is presented and some preliminary results are reported.
 
Online user profiling is a very active research field, catalyzing great interest by both scientists and practitioners. In this paper, in particular, authors look at approaches able to mine social media activities of users to create a rich user profile. Authors look at the case in which the profiling is meant to characterize the user's interests along a set of predefined dimensions (that authors refer to as [[categories]]). A conventional way to do so is to use semantic analysis techniques to (i) extract relevant entities from the online conversations of users (ii) mapping said entities to the predefined categories of interest. While entity extraction is a well-understood topic, the mapping part lacks a reference standardized approach. In this paper authors propose using graph navigation techniques on the [[Wikipedia]] tree to achieve such a mapping. A prototypical implementation is presented and some preliminary results are reported.

Revision as of 08:20, 14 August 2020


A Wikipedia-Based Approach to Profiling Activities on Social Media
Authors
Christian Torrero
Carlo Caprini
Daniele Miorandi
Publication date
2018
Links
Original Preprint

A Wikipedia-Based Approach to Profiling Activities on Social Media - scientific work related to Wikipedia quality published in 2018, written by Christian Torrero, Carlo Caprini and Daniele Miorandi.

Overview

Online user profiling is a very active research field, catalyzing great interest by both scientists and practitioners. In this paper, in particular, authors look at approaches able to mine social media activities of users to create a rich user profile. Authors look at the case in which the profiling is meant to characterize the user's interests along a set of predefined dimensions (that authors refer to as categories). A conventional way to do so is to use semantic analysis techniques to (i) extract relevant entities from the online conversations of users (ii) mapping said entities to the predefined categories of interest. While entity extraction is a well-understood topic, the mapping part lacks a reference standardized approach. In this paper authors propose using graph navigation techniques on the Wikipedia tree to achieve such a mapping. A prototypical implementation is presented and some preliminary results are reported.