% by Mirella M. Moro; version: September/03/2010 @ 6:04pm 
% -- 09/03/2010: bib file with names for proceedings and journals; cls with shrinked {received}
% -- 08/27/2010: appendix, table example, more explanation within comments, editors' data

\documentclass[jidm,a4paper]{jidm} % NOTE: JIDM is published on A4 paper
\usepackage{graphicx,url}  % for using figures and url format
\usepackage[T1]{fontenc}   % avoids warnings such as "LaTeX Font Warning: Font shape 'OMS/cmtt/m/n' undefined"
\usepackage{algorithm}
\usepackage{algorithmic}
\usepackage{amsmath}
\usepackage{subfig}

\usepackage{url}

%\usepackage{cite} % NOTE: do **not** include this package because it conflicts with jidm.bst

% Standard definitions
\newtheorem{theorem}{Theorem}[section]
\newtheorem{conjecture}[theorem]{Conjecture}
\newtheorem{corollary}[theorem]{Corollary}
\newtheorem{proposition}[theorem]{Proposition}
\newtheorem{lemma}[theorem]{Lemma}
\newdef{definition}[theorem]{Definition}
\newdef{remark}[theorem]{Remark}
\newenvironment{latexcode}
{\vspace{0.1in}\setlength{\parindent}{18pt}}
{\vspace{0.1in}}


\newcommand{\meira}[1]{{\bf MEIRA: #1}}
\newcommand{\pcalais}[1]{{\bf PCALAIS: #1}}
\newcommand{\dorgival}[1]{{\bf LOIC: #1}}
%\newcommand{\pcalais}[1]{}
%\newcommand{\meira}[1]{}
%\newcommand{\dorgival}[1]{}

\newcommand{\ie}{i.\,e.}
\newcommand{\Ie}{I.\,e.}
\newcommand{\eg}{e.\,g.}
\newcommand{\Eg}{E.\,g.}

% ALL FIELDS DOWN TO BEGIN{document} ARE MANDATORY

% The following data (volume, number and page) are given by the editors prior to publishing your paper
\jidmVolume{1}
\jidmNumber{4}
\jidmYear{11}
\jidmMonth{October}
\setcounter{page}{1}


% Includes headers with simplified name of the authors and paper title
\markboth{Pedro H. Calais Guerra et al}
%{Learning User Bias by Temporal Locality in Microblogging Platforms}
{Exploiting Temporal Locality to Determine User Bias in Microblogging Platforms}
%  -> \markboth{}{}
%         takes 2 arguments
%         ex: \markboth{M. M. Moro}{Any paper title}


% Title of the paper
\title{Exploiting Temporal Locality to Determine User Bias in Microblogging Platforms}
%\title{JIDM - Journal of Information \\ and Data Management}


% List of authors
%IF THERE ARE TWO or more institutions, please use:
%\author{Name of Author1\inst{1}, Name of Author2\inst{2}, Name of Author3\inst{2}}
\author{Pedro H. Calais Guerra, Lo\"{i}c Cerf, Thiago C. Porto \\ Adriano Veloso, Wagner Meira Jr. and Virgilio Almeida}


%Affiliation and email
\institute{Universidade Federal de Minas Gerais, Brazil \\ Computer Science Department \\ \email{\{pcalais,lcerf,tcp,adrianov,meira,virgilio\}@dcc.ufmg.br}
% IF THERE IS ANOTHER INSTITUTION:
%\and Name_of_the_second_institution \\
%\email{address@whatever.com}
}


% Paper abstract - it should be from 100 to 300 words
\begin{abstract}
Bias is the human tendency to favor one side of a discussion in argumentation,
lacking neutrality and balance. Determining user biases is  key to applications
that process, interpret, and recommend content generated by those users in
social media platforms. This paper addresses the problem of determining (in a
supervised way) biases of microbloggers from the stream of messages. In this paper,
we evaluate the use of a new criterion to identify user bias in social media
systems: the temporal locality among users that have similar
bias, \ie, the fact that people having similar biases express at about the
same time. We show that this remarkable property indeed holds in some
domains discussed in Twitter and may be explained mainly by the real-time use
of the microblogging platform, \ie, users with similar biases react altogether
to the outcome of events that are in accordance with their opinion (\eg, their
favorite soccer teams scores a goal). Besides the precision of the computed biases, our proposal presents two major
advantages that are consequences of not considering content at all (only
temporal information is used). First, it is very efficient, \ie, a modest
hardware can process on the fly the whole stream of messages about a popular
topic commented in Twitter. Second, we believe that it may be applied to a wide
range of domains regardless the language in which the messages are written. The
experimental section of this paper reports the efficient learning of precise
biases in both sportive and political contexts where the numerous messages are
either written in English or in Portuguese.
\end{abstract}


% ACM Computing Classification System categories
\category{H. Information Systems}{H.m. Miscellaneous}{Data Mining}
% Categories and Descriptors are available at the 1998 ACM Computing Classification System
% http://www.acm.org/about/class/1998/
%  -> \category{}{}{}
%         takes 3 arguments for the Computing Reviews Classification Scheme.
%         ex: \category{D.3.3}{Programming Languages}{Language Constructs and Features}
%                   [data types and structures]
%                   the last argument, in square brackets, is optional.

% Paper keywords
\keywords{microblogs, opinion mining, social media, supervised
  learning, user bias, user profiling}
%  -> \keywords{} (in alphabetical order \keywords{document processing, sequences,
%                      string searching, subsequences, substrings})




\begin{document} 
\begin{bottomstuff}
This work was partially supported by CNPq, CAPES, FAPEMIG, FINEP and
UOL (\url{www.uol.com.br}), through its Research Scholarship Program,
Proc. Number 20110215235100.
\end{bottomstuff}

\maketitle

\section{Introduction}
\label{sec:introduction}
\input{introduction}


\section{Related Work}
\label{sec:relatedWork}
\input{relatedWork}


\section{Learning User Bias by Temporal Locality}
\label{sec:algorithm}
\input{algorithm}

\section{Experimental Results}
\label{sec:results}
\input{results}

\section{Conclusions and Outlook}
\label{sec:conclusion}
\input{conclusion}

\bibliographystyle{jidm}
\bibliography{references}

\end{document}
