\documentclass[11pt,twoside]{article}\makeatletter \IfFileExists{xcolor.sty}% {\RequirePackage{xcolor}}% {\RequirePackage{color}} \usepackage{colortbl} \usepackage{wrapfig} \usepackage{ifxetex} \ifxetex \usepackage{fontspec} \usepackage{xunicode} \catcode`⃥=\active \def⃥{\textbackslash} \catcode`❴=\active \def❴{\{} \catcode`❵=\active \def❵{\}} \def\textJapanese{\fontspec{Noto Sans CJK JP}} \def\textChinese{\fontspec{Noto Sans CJK SC}} \def\textKorean{\fontspec{Noto Sans CJK KR}} \setmonofont{DejaVu Sans Mono} \else \IfFileExists{utf8x.def}% {\usepackage[utf8x]{inputenc} \PrerenderUnicode{–} }% {\usepackage[utf8]{inputenc}} \usepackage[english]{babel} \usepackage[T1]{fontenc} \usepackage{float} \usepackage[]{ucs} \uc@dclc{8421}{default}{\textbackslash } \uc@dclc{10100}{default}{\{} \uc@dclc{10101}{default}{\}} \uc@dclc{8491}{default}{\AA{}} \uc@dclc{8239}{default}{\,} \uc@dclc{20154}{default}{ } \uc@dclc{10148}{default}{>} \def\textschwa{\rotatebox{-90}{e}} \def\textJapanese{} \def\textChinese{} \IfFileExists{tipa.sty}{\usepackage{tipa}}{} \fi \def\exampleFont{\ttfamily\small} \DeclareTextSymbol{\textpi}{OML}{25} \usepackage{relsize} \RequirePackage{array} \def\@testpach{\@chclass \ifnum \@lastchclass=6 \@ne \@chnum \@ne \else \ifnum \@lastchclass=7 5 \else \ifnum \@lastchclass=8 \tw@ \else \ifnum \@lastchclass=9 \thr@@ \else \z@ \ifnum \@lastchclass = 10 \else \edef\@nextchar{\expandafter\string\@nextchar}% \@chnum \if \@nextchar c\z@ \else \if \@nextchar l\@ne \else \if \@nextchar r\tw@ \else \z@ \@chclass \if\@nextchar |\@ne \else \if \@nextchar !6 \else \if \@nextchar @7 \else \if \@nextchar (8 \else \if \@nextchar )9 \else 10 \@chnum \if \@nextchar m\thr@@\else \if \@nextchar p4 \else \if \@nextchar b5 \else \z@ \@chclass \z@ \@preamerr \z@ \fi \fi \fi \fi \fi \fi \fi \fi \fi \fi \fi \fi \fi \fi \fi \fi} \gdef\arraybackslash{\let\\=\@arraycr} \def\@textsubscript#1{{\m@th\ensuremath{_{\mbox{\fontsize\sf@size\z@#1}}}}} \def\Panel#1#2#3#4{\multicolumn{#3}{){\columncolor{#2}}#4}{#1}} \def\abbr{} \def\corr{} \def\expan{} \def\gap{} \def\orig{} \def\reg{} \def\ref{} \def\sic{} \def\persName{}\def\name{} \def\placeName{} \def\orgName{} \def\textcal#1{{\fontspec{Lucida Calligraphy}#1}} \def\textgothic#1{{\fontspec{Lucida Blackletter}#1}} \def\textlarge#1{{\large #1}} \def\textoverbar#1{\ensuremath{\overline{#1}}} \def\textquoted#1{‘#1’} \def\textsmall#1{{\small #1}} \def\textsubscript#1{\@textsubscript{\selectfont#1}} \def\textxi{\ensuremath{\xi}} \def\titlem{\itshape} \newenvironment{biblfree}{}{\ifvmode\par\fi } \newenvironment{bibl}{}{} \newenvironment{byline}{\vskip6pt\itshape\fontsize{16pt}{18pt}\selectfont}{\par } \newenvironment{citbibl}{}{\ifvmode\par\fi } \newenvironment{docAuthor}{\ifvmode\vskip4pt\fontsize{16pt}{18pt}\selectfont\fi\itshape}{\ifvmode\par\fi } \newenvironment{docDate}{}{\ifvmode\par\fi } \newenvironment{docImprint}{\vskip 6pt}{\ifvmode\par\fi } \newenvironment{docTitle}{\vskip6pt\bfseries\fontsize{22pt}{25pt}\selectfont}{\par } \newenvironment{msHead}{\vskip 6pt}{\par} \newenvironment{msItem}{\vskip 6pt}{\par} \newenvironment{rubric}{}{} \newenvironment{titlePart}{}{\par } \newcolumntype{L}[1]{){\raggedright\arraybackslash}p{#1}} \newcolumntype{C}[1]{){\centering\arraybackslash}p{#1}} \newcolumntype{R}[1]{){\raggedleft\arraybackslash}p{#1}} \newcolumntype{P}[1]{){\arraybackslash}p{#1}} \newcolumntype{B}[1]{){\arraybackslash}b{#1}} \newcolumntype{M}[1]{){\arraybackslash}m{#1}} \definecolor{label}{gray}{0.75} \def\unusedattribute#1{\sout{\textcolor{label}{#1}}} \DeclareRobustCommand*{\xref}{\hyper@normalise\xref@} \def\xref@#1#2{\hyper@linkurl{#2}{#1}} \begingroup \catcode`\_=\active \gdef_#1{\ensuremath{\sb{\mathrm{#1}}}} \endgroup \mathcode`\_=\string"8000 \catcode`\_=12\relax \usepackage[a4paper,twoside,lmargin=1in,rmargin=1in,tmargin=1in,bmargin=1in,marginparwidth=0.75in]{geometry} \usepackage{framed} \definecolor{shadecolor}{gray}{0.95} \usepackage{longtable} \usepackage[normalem]{ulem} \usepackage{fancyvrb} \usepackage{fancyhdr} \usepackage{graphicx} \usepackage{marginnote} \renewcommand{\@cite}[1]{#1} \renewcommand*{\marginfont}{\itshape\footnotesize} \def\Gin@extensions{.pdf,.png,.jpg,.mps,.tif} \pagestyle{fancy} \usepackage[pdftitle={India Elections 2014: Time-Lagged Correlation between Media Bias and Facebook Trend}, pdfauthor={}]{hyperref} \hyperbaseurl{} \paperwidth210mm \paperheight297mm \def\@pnumwidth{1.55em} \def\@tocrmarg {2.55em} \def\@dotsep{4.5} \setcounter{tocdepth}{3} \clubpenalty=8000 \emergencystretch 3em \hbadness=4000 \hyphenpenalty=400 \pretolerance=750 \tolerance=2000 \vbadness=4000 \widowpenalty=10000 \renewcommand\section{\@startsection {section}{1}{\z@}% {-1.75ex \@plus -0.5ex \@minus -.2ex}% {0.5ex \@plus .2ex}% {\reset@font\Large\bfseries}} \renewcommand\subsection{\@startsection{subsection}{2}{\z@}% {-1.75ex\@plus -0.5ex \@minus- .2ex}% {0.5ex \@plus .2ex}% {\reset@font\Large}} \renewcommand\subsubsection{\@startsection{subsubsection}{3}{\z@}% {-1.5ex\@plus -0.35ex \@minus -.2ex}% {0.5ex \@plus .2ex}% {\reset@font\large}} \renewcommand\paragraph{\@startsection{paragraph}{4}{\z@}% {-1ex \@plus-0.35ex \@minus -0.2ex}% {0.5ex \@plus .2ex}% {\reset@font\normalsize}} \renewcommand\subparagraph{\@startsection{subparagraph}{5}{\parindent}% {1.5ex \@plus1ex \@minus .2ex}% {-1em}% {\reset@font\normalsize\bfseries}} \def\l@section#1#2{\addpenalty{\@secpenalty} \addvspace{1.0em plus 1pt} \@tempdima 1.5em \begingroup \parindent \z@ \rightskip \@pnumwidth \parfillskip -\@pnumwidth \bfseries \leavevmode #1\hfil \hbox to\@pnumwidth{\hss #2}\par \endgroup} \def\l@subsection{\@dottedtocline{2}{1.5em}{2.3em}} \def\l@subsubsection{\@dottedtocline{3}{3.8em}{3.2em}} \def\l@paragraph{\@dottedtocline{4}{7.0em}{4.1em}} \def\l@subparagraph{\@dottedtocline{5}{10em}{5em}} \@ifundefined{c@section}{\newcounter{section}}{} \@ifundefined{c@chapter}{\newcounter{chapter}}{} \newif\if@mainmatter \@mainmattertrue \def\chaptername{Chapter} \def\frontmatter{% \pagenumbering{roman} \def\thechapter{\@roman\c@chapter} \def\theHchapter{\roman{chapter}} \def\thesection{\@roman\c@section} \def\theHsection{\roman{section}} \def\@chapapp{}% } \def\mainmatter{% \cleardoublepage \def\thechapter{\@arabic\c@chapter} \setcounter{chapter}{0} \setcounter{section}{0} \pagenumbering{arabic} \setcounter{secnumdepth}{6} \def\@chapapp{\chaptername}% \def\theHchapter{\arabic{chapter}} \def\thesection{\@arabic\c@section} \def\theHsection{\arabic{section}} } \def\backmatter{% \cleardoublepage \setcounter{chapter}{0} \setcounter{section}{0} \setcounter{secnumdepth}{2} \def\@chapapp{\appendixname}% \def\thechapter{\@Alph\c@chapter} \def\theHchapter{\Alph{chapter}} \appendix } \newenvironment{bibitemlist}[1]{% \list{\@biblabel{\@arabic\c@enumiv}}% {\settowidth\labelwidth{\@biblabel{#1}}% \leftmargin\labelwidth \advance\leftmargin\labelsep \@openbib@code \usecounter{enumiv}% \let\p@enumiv\@empty \renewcommand\theenumiv{\@arabic\c@enumiv}% }% \sloppy \clubpenalty4000 \@clubpenalty \clubpenalty \widowpenalty4000% \sfcode`\.\@m}% {\def\@noitemerr {\@latex@warning{Empty `bibitemlist' environment}}% \endlist} \def\tableofcontents{\section*{\contentsname}\@starttoc{toc}} \parskip0pt \parindent1em \def\Panel#1#2#3#4{\multicolumn{#3}{){\columncolor{#2}}#4}{#1}} \newenvironment{reflist}{% \begin{raggedright}\begin{list}{} {% \setlength{\topsep}{0pt}% \setlength{\rightmargin}{0.25in}% \setlength{\itemsep}{0pt}% \setlength{\itemindent}{0pt}% \setlength{\parskip}{0pt}% \setlength{\parsep}{2pt}% \def\makelabel##1{\itshape ##1}}% } {\end{list}\end{raggedright}} \newenvironment{sansreflist}{% \begin{raggedright}\begin{list}{} {% \setlength{\topsep}{0pt}% \setlength{\rightmargin}{0.25in}% \setlength{\itemindent}{0pt}% \setlength{\parskip}{0pt}% \setlength{\itemsep}{0pt}% \setlength{\parsep}{2pt}% \def\makelabel##1{\upshape ##1}}% } {\end{list}\end{raggedright}} \newenvironment{specHead}[2]% {\vspace{20pt}\hrule\vspace{10pt}% \phantomsection\label{#1}\markright{#2}% \pdfbookmark[2]{#2}{#1}% \hspace{-0.75in}{\bfseries\fontsize{16pt}{18pt}\selectfont#2}% }{} \def\TheFullDate{2015 2015-05-15 (revised: Year 2015 15 May 2015)} \def\TheID{\makeatother } \def\TheDate{2015 2015-05-15} \title{India Elections 2014: Time-Lagged Correlation between Media Bias and Facebook Trend} \author{}\makeatletter \makeatletter \newcommand*{\cleartoleftpage}{% \clearpage \if@twoside \ifodd\c@page \hbox{}\newpage \if@twocolumn \hbox{}\newpage \fi \fi \fi } \makeatother \makeatletter \thispagestyle{empty} \markright{\@title}\markboth{\@title}{\@author} \renewcommand\small{\@setfontsize\small{9pt}{11pt}\abovedisplayskip 8.5\p@ plus3\p@ minus4\p@ \belowdisplayskip \abovedisplayskip \abovedisplayshortskip \z@ plus2\p@ \belowdisplayshortskip 4\p@ plus2\p@ minus2\p@ \def\@listi{\leftmargin\leftmargini \topsep 2\p@ plus1\p@ minus1\p@ \parsep 2\p@ plus\p@ minus\p@ \itemsep 1pt} } \makeatother \fvset{frame=single,numberblanklines=false,xleftmargin=5mm,xrightmargin=5mm} \fancyhf{} \setlength{\headheight}{14pt} \fancyhead[LE]{\bfseries\leftmark} \fancyhead[RO]{\bfseries\rightmark} \fancyfoot[RO]{} \fancyfoot[CO]{\thepage} \fancyfoot[LO]{\TheID} \fancyfoot[LE]{} \fancyfoot[CE]{\thepage} \fancyfoot[RE]{\TheID} \hypersetup{citebordercolor=0.75 0.75 0.75,linkbordercolor=0.75 0.75 0.75,urlbordercolor=0.75 0.75 0.75,bookmarksnumbered=true} \fancypagestyle{plain}{\fancyhead{}\renewcommand{\headrulewidth}{0pt}} \date{} \usepackage{authblk} \providecommand{\keywords}[1] { \footnotesize \textbf{\textit{Index terms---}} #1 } \usepackage{graphicx,xcolor} \definecolor{GJBlue}{HTML}{273B81} \definecolor{GJLightBlue}{HTML}{0A9DD9} \definecolor{GJMediumGrey}{HTML}{6D6E70} \definecolor{GJLightGrey}{HTML}{929497} \renewenvironment{abstract}{% \setlength{\parindent}{0pt}\raggedright \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt \textcolor{GJBlue}{\large\bfseries\abstractname\space} }{% \vskip8pt \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt } \usepackage[absolute,overlay]{textpos} \makeatother \usepackage{lineno} \linenumbers \begin{document} \author[1]{Francis P. Barclay} \author[2]{C. Pichandy} \author[3]{Anusha Venkat} \affil[1]{ PSG College of Arts and Science} \renewcommand\Authands{ and } \date{\small \em Received: 8 April 2015 Accepted: 5 May 2015 Published: 15 May 2015} \maketitle \begin{abstract} After establishing print media bias and Facebook trend as reliable predictors of election outcome, the study analyses the relationship between the two before and during the 2014 Indian Lok Sabha election. Time-lagged correlation is used to study the immediate effect of newspaper reports on the political behaviour of Facebook. Further, a correlation was found to exist between the long-term political trends in the print media and Facebook. That is, the number of positive and negative news reports published on a party in the newspapers affected the number of ?likes? recorded on the Facebook fan page of the party or its candidate. \end{abstract} \keywords{} \begin{textblock*}{18cm}(1cm,1cm) % {block width} (coords) \textcolor{GJBlue}{\LARGE Global Journals \LaTeX\ JournalKaleidoscope\texttrademark} \end{textblock*} \begin{textblock*}{18cm}(1.4cm,1.5cm) % {block width} (coords) \textcolor{GJBlue}{\footnotesize \\ Artificial Intelligence formulated this projection for compatibility purposes from the original article published at Global Journals. However, this technology is currently in beta. \emph{Therefore, kindly ignore odd layouts, missed formulae, text, tables, or figures.}} \end{textblock*} \let\tabcellsep& \section[{INTRODUCTION}]{INTRODUCTION}\par edia effect on the masses is an intenselystudied area of communications research, but the relationship that exists between different media platforms and they way they interact and influence each other have been barely explored. Print media remains to be a main source of political information in India and it also influences voter decisions \hyperref[b2]{(Chiang and Knight, 2011)}. On the other hand, the online social media has become a tool for free expression of political opinion-its content being user generated \hyperref[b17]{(Woolley, et al., 2010)}. Hence, it can be consciously and cautiously assumed that print media behaviour could affect the political trend observed on online social media. To test this theory in the context of the 2014 Indian Parliamentary election, four leading English newspapers and Facebook were chosen.\par Perhaps, India's 2014 general election was the world's largest democratic exercise-with about 814.5 million eligible voters-conducted in nine phases from April 7 till May 12, 2014. The Indian National Congress (INC or just the Congress), Bharatiya Janata Party (BJP) and the Aam Aadmi Party (AAP) were the dominant parties contesting the election. Arvind Kejriwal led the AAP, while Rahul Gandhi, son of former Prime Minister Rajiv Gandhi and Congress President Sonia Gandhi, was portrayed as the face of INC. BJP nominated Narendra Damodardas Modi as its prime ministerial candidate, who led his party to a thumbing victory.\par While the Congress party secured 106,935,311 (19.3\%) votes, the BJP amassed 171,637,684 (31\%). AAP managed a meagre 11,325,635.\par Both the mainstream print and the online social media played decisive roles during the election period, spreading political news and moulding public opinion \hyperref[b10]{(Pansare, 2014;}\hyperref[b13]{and Swamy, 2014)}. In India, newspapers are witnessing steady growth in circulation numbers bucking the global trend \hyperref[b7]{(Hooke, 2012)}. India is home to the world's largest English-language newspaper readership \hyperref[b6]{(Hayden, 2012)} and the fastest growing newspaper market (The Economist, 2011, and All About Newspapers, 2010). This apart, India recorded the fastest Facebook growth in 2014 {\ref (PTI, 2014)}. Facebook announced on March 31, 2014, that its Indian user base had just crossed the 100-million milestone \hyperref[b12]{(Singh, 2014)}.\par As the circulation of newspapers and the number of Facebook users increase, their influence on the electorate is only amplified. Hence, it becomes important to study their political behaviour during elections. Accordingly, the aim of the present study is stated to be:\par To identify the political trends that prevailed in the print media and Facebook during the period of study-January 24 to {\ref May 12, 2014.} To validate those political trends by correlating them with the election results.\par To associate the political trends by correlating them with each other.\par For the present study, the top four English newspapers published from India-readership wisewere chosen. By content analysing the newspapers, the political trend that prevailed during the study period can be estimated using time-series analysis.\par Similarly, the top national parties chosen for the study were the Congress, BJP and AAP, as the prospects of others claiming a majority at the Centre were bleak.\par Political influence of print media: Bartels (1993) analysed the persuasive effects of media exposure and concluded that new information absorbed via media exposure must be about three times as distinctive as has generally been supposed in order to account for observed patterns of opinion change. \hyperref[b11]{Rhee (1997)} found that news frames in election coverage affected individuals' interpretation of campaigns. \hyperref[b3]{Druckman and Parkin (2005)} investigated how editorial slant-defined as the quantity and tone of a newspaper's candidate coverage as influenced by its editorial position-shaped candidate evaluations and vote choice. Combining comprehensive content analyses of the papers with an Election Day exit poll, the researchers assessed the slant of campaign coverage and its effects on voters. The researchers claimed to have found compelling evidence that editorial slant affected electoral decisions. Exposure to newspapers affects political behaviour and opinion \hyperref[b5]{(Gerber, et al., 2006)}. \hyperref[b9]{Kuypers (2002)} charted the potential effects that the press has upon the messages of political and social leaders when they discuss controversial issues. \hyperref[b4]{Endersby (2011)} observed that news consumers who read papers are more likely to modify their perceptions of party ideology in the direction of press bias. Furthermore, media consumption correlated with ideological preferences and perceptions of political parties. Reviewing the past studies, it can be cautiously assumed that the print media could exhibit bias in their election coverage-and thereby, influence the voters. Candidates or their campaign staff then could personalise the profile with everything from photographs to qualifications for office. Facebook members could view these entries and register their support for specific candidates. They also received notification every time one of their Facebook friends registered support for a candidate. Facebook displayed the number of supporters for each candidate and calculated the percentage of -votes that candidate had in his or her race. The study found that the number of Facebook supporters was an indicator of a campaign resource that did matter, and was independent of the impact of other variables in their predictive model. This theory is applied in the present study as well, however, instead of the profile pages that were created for the US Congressional candidates, the Indian politicians have verified fan pages functioning on the social network. \hyperref[b8]{Kushin and Kitchener (2009)} studied the emergence of Facebook as a platform for political discourse and raised new questions for study of online political discussion as it occurred in the emergent Internet technologies of social network sites. \hyperref[b15]{Vitak (2011)} observed that in the 2008 US presidential election, social network sites such as Facebook allowed users to share their political beliefs, support specific candidates and interact with others on political issues. The researchers also found evidence that political activities on Facebook affected political participation among young voters. \section[{Political communication on}]{Political communication on}\par Reviewing the past studies, it can be inferred that various aspects of Facebook and political communication happening on the social network has been studied and the online social network is recognised as a crucial tool for propaganda and political deliberation. However, using the number of 'likes' recorded on Facebook fan pages and predicting election outcomes have not been studied, at least, in the studies reviewed. Facebook, like a few other online platforms, offers users the power to freely express their political opinion. The present study uses the count of 'likes' recorded on the Facebook fan pages of 'Narendra Modi', 'Arvind Kejriwal' and 'Indian National Congress' during the study period to trace the political trend of Facebook.\par Furthermore, time-that is an important aspect of communication-has been ignored in the past studies. But the present study employs reliable methodologies to study the political trends in the print media and on Facebook, and track their shift over time. On Facebook, the number of 'likes' recorded on the fan pages of the politicians can help estimate the political trend prevailing on the social network. But for the newspapers, the political polarity of each of the political news items published on the three parties-that is, whether it is favourable, unfavourable or neutral-and their position in the papers-that is, on which page, the news items were published-have to be considered to track the political trend. a) Research questions At this stage, to bring in some focus to the study, the following research questions are asked: RQ1. Do the newspapers show a particular partisan political orientation? RQ2. Is the political trend in the papers associated with the election results? RQ3. Which political party is popular on Facebook and to what extent? RQ4. Is Facebook popularity associated with the election results? RQ5. Is the press trend associated with the Facebook popularity? b) Hypotheses for the newspapers Press popularity can be defined in many ways. Even the number of mentions of a party name could be used to estimate popularity. But the present study is focussed on studying latent content rather than manifest content as the former is considered more meaningful. Since, the study considers the political polarities of political news items and their respective positions in the paper, the following hypothesis is proposed for the newspapers:\par Volume XV Issue II Version I 30 ( ) More number of strategically-positioned positive reports on a party in the newspapers means more votes for that party in the election. This hypothesis takes into account only the positive reports published on a particular party. Newspapers publish both positive and negative reports and hence, another hypothesis is proposed to take into account the negative reports as well:\par More number of strategically-positioned positive reports and comparatively lesser number of strategically-positioned negative reports on a party in the newspapers means more votes for that party in the election. This hypothesis could be simplified by introducing a term 'positivity' which will stand for 'strategically-positioned positive reports and comparatively lesser strategically-positioned negative reports' as: More positivity for a party in the newspapers means more votes for that party in the election.\par Testing this hypothesis will answer RQ1 and RQ2. c) Hypotheses for Facebook On Facebook, leaders of two of the three chosen parties had verified fan pages. However, for the other party, the fan page of the party was taken into account for analysis. The number of 'likes' recorded on the fan pages of the representative of the parties were more than that of the party, which means, the leaders were more popular that their respective parties on social media.\par Hence, the hypothesis for Facebook would be: More number of 'likes' recorded on the fan page of a party or the representative of the party means more votes for that party in the election.\par Likes are termed positive. However, the number of 'likes' keeps adding up. At the start of the study, the parties had different number of 'likes' and hence, calculations based on them could be erratic. Hence, to track the actual political trend on Facebook, the number of new 'likes' recorded every day was taken into account. For that analysis, the hypothesis is as follows:\par More number of new 'likes' recorded on the fan page of a party or the representative of the party means more votes for that party in the election.\par Testing this hypothesis will answer RQ 3 and RQ 4. \section[{d) Hypotheses for print media-Facebook association}]{d) Hypotheses for print media-Facebook association}\par The above-mentioned hypotheses identify the political trends that prevailed in the print media and Facebook during the period of study and validate them. But the present study also seeks to analyse short-and long-term effects of the print media on the people and Facebook by comparing the content of newspapers with that of the online social network. People are the connection between mass media and the online social media. So, if the trends and shocks observed in the press is reflected and felt on the online social media then the amount of influence that these media exert on the people and vice-versa can be deduced. Hence, the following hypothesis is proposed to infer and check the effects of the print media:\par More positivity for a party in the newspapers means more number of new 'likes' recorded on the fan page of a party or the representative of the party.\par After the hypotheses are proposed, the next step would be to choose appropriate methods of research. The method of research chosen is explained in the following chapter. \section[{II.}]{II.} \section[{METHOD OF RESEARCH a) Newspapers}]{METHOD OF RESEARCH a) Newspapers}\par Based on readership figures, the following broadsheet dailies were selected for the study: The Times of India (ToI), Hindustan Times (HT), The Hindu (TH) and The Telegraph (TT). Political news items published in the chosen four newspapers were collected on a daily-basis from January 24 to May 12, 2014-the period of study. In this study, 'news item' refers to news stories, editorials, op-ed pieces, columns, standalone pictures, info-graphics and opinion pieces published in the newspapers. The unit of analysis is a news item. Of the news items published, the ones that were related to the chosen parties-the Congress, BJP and AAP-were segregated.\par Each of the items was analysed and classified as positive, neutral or negative for a party based on its content. A common formula was applied to each of the news item in this comparative study to mitigate any inherent bias in the data analysis.\par Scoring guidelines for polarity: Nine categories were chosen for categorisation of political polarity-Congress positive, Congress negative and Congress neutral; BJP positive, BJP negative and BJP neutral; AAP positive, AAP negative and AAP neutral. While reporting an issue or controversy, if a news item presented the view or statement of a party or the views that favour that party, then the news item was classified as positive for that party.\par In the case of multiple views, the dominant view was considered.\par If a news item had the mention of a party and was found to be damaging the image of that party, it was rated as negative. If a news item was based on the political campaign of a party, then it was classified as positive for that party. Positive and negative statements were tracked in news reports to decide their polarity. If a news item mentions more than one party, then the party that is dominantly discussed in the news report is considered.\par A news item that did not exhibit a perceivable political polarity was categorised as neutral. Only the news items that exhibited a political polarity-that is, either positive or negative-were considered for further analysis of political orientation of the newspapers. \section[{Year 2015}]{Year 2015}\par India Elections 2014: Time-Lagged Correlation between Media Bias and Facebook Trend A Scoring guidelines for position: Based on the position of the news item in the paper-that is on which page it appeared-weightage was assigned to it. Front page news item -5; editorial -4; news item on editorial or oped page -3 and news item on nation page -2.\par Independent variables in this study are newspaper, party and 'time', while the dependent variables are political polarity and the position of news items in the paper, which were measured in ratio points. Political orientation of a newspaper was calculated based on the two dependent variables. Calculation was done daily to track the trend over time as the four papers analysed are daily broadsheets. For the independent variable time, the unit of measurement was one day.\par Reliability: An 'a priori' coding scheme describing all the measures was created and the scoring guidelines were served to the coders, who were trained with samples before the study period. Since a human coding method was employed, the meaning and content of the news items were better analysed to estimate the political orientation of the chosen four newspapers.\par Inter-coder reliability was tested. Cohen's ? was run to determine if there was agreement between two coders using a sample of 50 news items and the guidelines proposed.\par There was almost perfect agreement between the coders' judgments, ? = .856 (Std. error .055), p < .0005. \section[{b) Facebook}]{b) Facebook}\par For the content analysis of Facebook, the official verified fan pages of Narendra Modi and Arvind Kejriwal were chosen to represent their respective parties-BJP and AAP. Rahul Gandhi did not have a verified fan page on Facebook. However, during the middle of the study, through promotional campaigns and adverts, the Indian National Congress party publicised its website and official Facebook fan page. After that, the Indian National Congress official fan page was chosen to represent the Congress party on Facebook. The number of 'likes' recorded on these fan pages were recorded on a daily-basis during the study period-January 24 to May 12, 2014.\par The unit of analysis is a 'like'. The number of 'likes' for a day was randomly recorded at different times during the day. However, the number of 'likes' on each of the fan pages was recorded at the same time during a day to mitigate bias and inter-subjectivity. Since a 'like' carries a positive character and there was not a negative equivalent to it on Facebook, the number of 'likes' was only used to understand the political trend on Facebook. Independent variables are party and 'time', while the dependent variable is 'like', which were measured in ratio points. Political trend that prevailed on Facebook was deduced using the counts of 'likes' that were recorded on each of the chosen fan pages. It was recorded daily to track the trend over time. For the independent variable time, the unit of measurement was one day. Political trends were calculated for the whole study period to conclude which party was favoured and to what extent on Facebook. The political trend of Facebook was determined using the number of actual 'likes' and the number of new 'likes' recorded on the fan pages.\par The data were collected and analysed using Microsoft Excel spreadsheet and a portable version of the SPSS statistics software. For the time-series analyses, linear and quadratic regression models and SPSS Expert Modeler were employed. \section[{III.}]{III.}\par FINDINGS AND DISCUSSION RQ1. Do the newspapers show a particular partisan political orientation?\par Political news published in the four chosen newspapers-The Times of India, Hindustan Times, The Hindu and The Telegraph-were reviewed on a daily basis during the period of study-January 24 to May 12, 2014. Of them, the political news items published on the front page, editorial page, Op-ed page and nation pages on the three national parties selected for the study were rated as positive and negative for a party. That was identified as the political polarity of a news item. Based on the news item's position in the paperthat is, on which page it was published-each news item was assigned a weightage. Daily scores for the papers were calculated by summing up the weightages of the positive and negative news items. The first part of the analysis deals with finding out the political orientations of the four newspapers-that is, which newspaper supported which political party and to what extent. \section[{a) One-way Anova: Party vs Positivity}]{a) One-way Anova: Party vs Positivity}\par To determine the political orientation of the newspapers, the political polarities identified in its reports with the position weightage attributed to each of the polarity were summed up for the whole study period. Then the negative scores were subtracted from the positive scores to get the positivity scores, which were used to define the political orientation of the newspapers.\par One-way Anova was performed to find out if there were statistically significant differences among the three chosen parties with regard to the positivity scores that each of them earned in the daily newspapers during the study period and the results are presented in Table1.\par Volume XV Issue II Version I 32 ( ) There was a statistically significant difference among the parties with regard to their average positivity scores as determined by one-way Anova (p < .0005). A Tukey post-hoc test revealed the political orientations of the newspapers (BJP-21.44; Congress-15.66 and AAP-6.55) were in favour of the BJP, see Table \hyperref[tab_1]{2}. The Aam Aadmi Party drew the least amount of support from the newspapers-that is, it saw the least number of favourable news reports. RQ2. Is the political trend in the papers associated with the election results?\par The daily positivity scores are summed up for the whole study period and these scores are used to find an association between press trends and election results. The sum of the overall positivity scores for the three chosen parties and the election results are tabled in Table \hyperref[tab_2]{3}. To measure the strength and direction of association that exists between press trends and elections results, Spearman's rank-order correlation coefficient was calculated and the test results are presented in Table \hyperref[tab_3]{4}. The coefficient will provide a nonparametric measure of association between the political trends in newspapers (media bias) and poll results, and will be used to test the hypothesis that press trends are positively associated with election results. A Spearman's rank-order correlation was run to determine the relationship between the political trend on the newspapers and election results. There was a strong, positive correlation between them, which was statistically significant (r s = 1.000, p < .01).\par Hence, the hypothesis that more positivity for a party in the newspapers means more votes for that party in the election is tenable. \section[{RQ3. Which political leader is popular on Facebook and}]{RQ3. Which political leader is popular on Facebook and}\par to what extent? BJP's prime ministerial candidate Narendra Modi and AAP's Arvind Kejriwal had verified 'Fan pages' in their respective names on Facebook. But Congress's Rahul Gandhi did not have one. Hence, for the study, the Facebook fan pages of 'Narendra Modi', 'Arvind Kejriwal' and 'Indian National Congress' were \section[{Year 2015}]{Year 2015}\par India Elections 2014: Time-Lagged Correlation between Media Bias and Facebook Trend A considered and the varying number of on each of those pages were recorded on a daily-basis for analysis. Each of these fan pages will represent one of the chosen parties-the Congress, BJP and Aam Aadmi Party.\par To estimate the political trend on Facebookthat is, how popular the party and politicians chosen for the study are on Facebook-a one-way Anova test was conducted which would identify statistically significant differences among them with regard to the number of new 'likes' recorded on their respective Facebook fan pages during the period of study. The results are presented in Table \hyperref[tab_4]{5}. There is a statistically significant difference among the three chosen parties with regard to the average number of new 'likes' that they secured each day during the period of study as determined by the one-way Anova (F(2,267) = 63.409, p < .0005), refer The average number of new 'likes' recorded during the period of study is used to find an association between Facebook political trends and election results, see Table \hyperref[tab_7]{7}. To measure the strength and direction of association that exists between Facebook's trends and elections results, Spearman's rank-order correlation coefficient was calculated and the test results are presented in Table \hyperref[tab_8]{8}. The coefficient will provide a nonparametric measure of association between the political trends on Facebook and poll results, and will be used to test the hypothesis that Facebook popularity is positively associated with election results. Spearman's rank-order correlation was run to determine the relationship between the political trends on Facebook and election results. There was a strong, positive correlation between them, which was statistically significant (r s = 1.000, p < .01).\par There is a strong correlation between the average number of new likes recorded during the study period and the election results.\par Hence, the hypothesis that more number of new 'likes' recorded on the fan page of a party or the representative of the party means more votes for that party in the election is tenable. \section[{RQ5. Is the press trend associated with the Facebook popularity?}]{RQ5. Is the press trend associated with the Facebook popularity?}\par Based on the polarity and position of political items published in the newspapers, daily scores were assigned to the parties under six categories-Congress positive, Congress negative, BJP positive, BJP negative, AAP positive and AAP negative. daily scores for the parties in the four newspapers combined were used to understand the underlying political trends in the newspapers. Similarly, the Facebook fan pages of 'Narendra Modi', 'Arvind Kejriwal' and 'Indian National Congress' were considered and the varying number of 'Likes' on each of those pages were recorded on a daily-basis for analysis. To measure the effect of press trends on Facebook, the daily positivity scores for the three parties chosen in the newspapers and the number of new 'likes' recorded on each day of the study period have to be correlated. The daily scores for the parties in the papers and Facebook are presented in Table \hyperref[tab_9]{9}. These positivity scores for the Congress, BJP and AAP, calculated daily, were used for further analysis. In time-series analysis, the first step would be to plot the data, to acquire a basic nature analysis best suit them. The scores of the three parties-Congress, BJP and AAP-are plotted in Fig. {\ref 1}.\par Figure {\ref 1} : Time-series plot for positivity scores Similarly, the daily increase in the number of 'likes' is plotted in Fig. {\ref 2}.\par Figure {\ref 2} : Time-series plot for daily increase in the number of Fan page 'likes' Analysing Fig. {\ref 1 and 2}, it is observed that the curves exhibit seasonal fluctuations hiding the underlying trend. It is also observed that the curves sport a trend and not stationary. A series is stationary if its mean and variance stay about the same over the length of the series.\par To estimate the immediate effect of the print media on Facebook using cross-correlation, the time- a. Based on the assumption that the series are not cross correlated and that one of the series is white noise.-\par The analysis shows that the press trend (positivity scores) for the Congress party is a leading indicator for the number of likes that the fan page of Indian National Congress secures. As shown in the plot (Fig. 3), most of the correlations are small. There is a fairly large negative correlation of ?0.179 at lag 2. A positive lag indicates that the first series leads the second series. It can be concluded that the leading indicator press trend of Congress really is a leading indicator and that it works best at predicting the value of new Facebook 'likes' two periods later. Similarly, the time-series for AAP in the newspapers and Facebook were cross-correlated and the results are presented in Fig. 5. In the case of AAP, too, the press trend (positivity scores) was found to be a leading indicator for number of likes that the fan page of Arvind Kejriwal secured. As shown in the plot (Fig. 5), most of the correlations are small. There is a fairly large negative correlation of ?0.174 at lag 0 and a fairly large positive correlation of 0.214 at lag 3. The leading indicator works best at predicting the value of Facebook 'likes' three days later. \section[{A}]{A}\par Though the time-lagged correlation coefficients found an association between the press and Facebook trends at several lags, those are about seasonal fluctuations. The study aims at establishing a correlation between the long term political trends of Facebook and the print media. Since the original curves are highly distorted by seasonal fluctuations, the curves are decomposed to extract the seasonal component by calculating the moving averages for seven periodssince data were collected on all the seven days of a week. After the seasonal component is removed from the original curves, the residual data is subjected to correlation analysis and the results of the analysis are presented in Table \hyperref[tab_11]{11}. A Pearson product-moment correlation was run to determine the relationship between the deseasonalised daily positivity scores that the Congress earned in the newspapers and the number of new 'likes' recorded on the Facebook fan page of the Indian National Congress during the study period. The data showed no violation of normality, linearity or homoscedasticity. There was a strong, positive correlation between the press and Facebook trends, which was statistically significant (r = .304, n = 51, p = .015). Similarly, there was a significant correlation between the deseasonalised daily positivity scores that the BJP earned in the newspapers and the number of new 'likes' recorded on the Facebook fan page of Narendra Modi during the study period (r = .177, n = 102, p = .038). The deseasonalised daily positivity scores that the AAP earned in the newspapers was also positively correlated with the number of new 'likes' recorded on the Facebook fan page of Arvind Kejriwal during the study period (r = .576, n = 102, p < .0005). \section[{IV.}]{IV.} \section[{CONCLUSION}]{CONCLUSION}\par As the results of the empirical analyses show, both the political trends in the print media and on Facebook were reliable predictors of the outcome of the 2014 Lok Sabha elections. Press trend or media bias (Congress -35.88\%; BJP -49.12\% and AAP -15.01\%) was highly in favour of the BJP party and correlated with the popular vote share of the parties in the polls that the BJP won. Similarly, the political trend on Facebook (Congress -25.67\%; BJP -58.6\% and AAP -15.73\%) that was tracked using the number of 'likes' recorded on the fan pages of the parties and their popular candidates was highly in favour of the BJP and correlated with the election results (Congress -36.89\%; BJP -59.21\% and AAP -3.91\%). That is, analysing the bias in the press reports published during the election, the probable winner of the elections can be predicted. In simple terms, more positive news and comparatively lesser amount of negative news published in the newspapers means more votes for the party in the elections. Similarly, the number of 'likes' recorded on the Facebook fan page of a party or its candidate can be used to estimate how popular the party or candidate is among the people. The study results have shown that the political trend on Facebook can also be used to predict the probable winner.\par The thread that connects the print media and Facebook are the people. People who read news reports published in the papers are influenced by it and when they lend their support to their favourite parties on Facebook, that influence is felt. This theory was found to be tenable through statistical tests. A correlation was found to exist between press and Facebook trends. That is, the effect of political news published in the papers during the election period was felt on Facebook with variations in the number of 'likes' recorded on the fan pages of the parties.\par The present study investigated both short-and long-term effects. Cross-correlation analyses were performed to estimate the immediate effects. It was found that in all three cases-the Congress, BJP and AAP-the press trend was a leading indicator. That is, the press trend can be used as a predictor for the Facebook trend. In other words, analysing the number of positive and negative reports published in the newspapers, the probable increase or decrease in the number of 'likes' recorded on the Facebook fan pages Volume XV Issue II Version I 40 ( ) can be predicted. However, the strongest amount of correlation between press and Facebook trends was found to be several lags away. That is, the effect of media reports on Facebook was not immediately felt, but several periods-in the present case, several days-later. The study was more interested in finding a correlation between the long-term political trends of the newspapers and Facebook.\par Positive correlations were reported indicating that the newspapers had an effect on Facebook-which in turn, shows that the newspapers had an effect on the people.\par Since media bias, Facebook trend and the electionresults correlated, the present study concludes that just by studying the content of a mass media that people avidly use, the outcome of the election-or any other future behaviour of the people-can be predicted.\begin{figure}[htbp] \noindent\textbf{}\includegraphics[]{image-2.png} \caption{\label{fig_0}}\end{figure} \begin{figure}[htbp] \noindent\textbf{1} \par \begin{longtable}{P{0.2141732283464567\textwidth}P{0.38149606299212596\textwidth}P{0.0468503937007874\textwidth}P{0.10039370078740158\textwidth}P{0.08031496062992126\textwidth}P{0.026771653543307086\textwidth}} \tabcellsep \multicolumn{3}{l}{Sum of Squares df Mean Square}\tabcellsep F\tabcellsep Sig.\\ Between Groups\tabcellsep 12284.642\tabcellsep 2\tabcellsep 6142.321\tabcellsep \multicolumn{2}{l}{19.907 .000}\\ Within Groups\tabcellsep 99972.275\tabcellsep 324\tabcellsep 308.556\tabcellsep \\ Total\tabcellsep 112256.917\tabcellsep 326\tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_0}Table 1 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{2} \par \begin{longtable}{P{0.0828616352201258\textwidth}P{0.08820754716981133\textwidth}P{0.01069182389937107\textwidth}P{0.12295597484276728\textwidth}P{0.10157232704402516\textwidth}P{0.16305031446540882\textwidth}P{0.03742138364779874\textwidth}P{0.08553459119496856\textwidth}P{0.10424528301886793\textwidth}P{0.05345911949685534\textwidth}} \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{95\% Confidence Interval}\\ \tabcellsep N\tabcellsep Mean\tabcellsep Std. Deviation\tabcellsep Std. Error\tabcellsep Lower\tabcellsep \multicolumn{2}{l}{for Mean Upper}\tabcellsep Minimum Maximum\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Bound}\tabcellsep Bound\\ \multicolumn{3}{l}{Congress 109 15.6606}\tabcellsep 19.24800\tabcellsep 1.84362\tabcellsep \multicolumn{2}{l}{12.0062}\tabcellsep 19.3149\tabcellsep -18.00\tabcellsep 84.00\\ BJP\tabcellsep \multicolumn{2}{l}{109 21.4404}\tabcellsep 21.09697\tabcellsep 2.02072\tabcellsep \multicolumn{2}{l}{17.4349}\tabcellsep 25.4458\tabcellsep -39.00\tabcellsep 70.00\\ AAP\tabcellsep \multicolumn{2}{l}{109 6.5505}\tabcellsep 10.49293\tabcellsep 1.00504\tabcellsep \multicolumn{2}{l}{4.5583}\tabcellsep 8.5426\tabcellsep -21.00\tabcellsep 34.00\\ Total\tabcellsep \multicolumn{2}{l}{327 14.5505}\tabcellsep 18.55657\tabcellsep 1.02618\tabcellsep \multicolumn{2}{l}{12.5317}\tabcellsep 16.5692\tabcellsep -39.00\tabcellsep 84.00\end{longtable} \par \caption{\label{tab_1}Table 2 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{3} \par \begin{longtable}{P{0.37447552447552446\textwidth}P{0.18426573426573425\textwidth}P{0.15454545454545454\textwidth}P{0.1367132867132867\textwidth}} \tabcellsep Congress\tabcellsep BJP\tabcellsep AAP\\ Press trend\tabcellsep 1707\tabcellsep 2337\tabcellsep 714\\ Popular vote share\tabcellsep 106760001\tabcellsep 171459286\tabcellsep 11325635\\ Press trend \%\tabcellsep 35.88\tabcellsep 49.12\tabcellsep 15.01\\ Popular vote share\%\tabcellsep 36.87\tabcellsep 59.22\tabcellsep 3.91\end{longtable} \par \caption{\label{tab_2}Table 3 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{4} \par \begin{longtable}{P{0.32241379310344825\textwidth}P{0.5275862068965518\textwidth}} Press trend\tabcellsep Popular vote share\end{longtable} \par \caption{\label{tab_3}Table 4 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{5} \par \begin{longtable}{P{0.17435897435897435\textwidth}P{0.33237179487179486\textwidth}P{0.04903846153846154\textwidth}P{0.2125\textwidth}P{0.03814102564102564\textwidth}P{0.04358974358974359\textwidth}} \tabcellsep Sum of Squares\tabcellsep df\tabcellsep Mean Square\tabcellsep F\tabcellsep Sig.\\ Between Groups\tabcellsep 75052301982.529\tabcellsep 2\tabcellsep 37526150991.265\tabcellsep 63.409\tabcellsep .000\\ Within Groups\tabcellsep 156830582914.706\tabcellsep 265\tabcellsep 591813520.433\tabcellsep \tabcellsep \\ Total\tabcellsep 231882884897.235\tabcellsep 267\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_4}Table 5 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{6} \par \begin{longtable}{P{0.85\textwidth}} more popular than the Indian National Congress\\ (21720.38 ± 14957.793 likes) and Arvind Kejriwal\\ (13307.04 ± 7084.970 likes) on Facebook. There was\\ no statistically significant difference between the\\ average numbers of new 'likes' recorded by INC and\\ Kejriwal (p = .103).\\ Modi (49589.44 ± 36178.298 likes, p < .0005) was\end{longtable} \par \caption{\label{tab_5}Table 6 .}\end{figure} \begin{figure}[htbp] \noindent\textbf{6} \par \begin{longtable}{P{0.363215859030837\textwidth}P{0.13480176211453745\textwidth}P{0.01497797356828194\textwidth}P{0.18348017621145374\textwidth}P{0.15352422907488986\textwidth}} \tabcellsep N\tabcellsep Mean\tabcellsep Std. Deviation\tabcellsep Std. Error\\ MODI\tabcellsep \multicolumn{2}{l}{108 49589.44}\tabcellsep 36178.298\tabcellsep 3481.258\\ INC\tabcellsep \multicolumn{2}{l}{52 21720.38}\tabcellsep 14957.793\tabcellsep 2074.273\\ \multicolumn{3}{l}{KEJRIWAL 108 13307.04}\tabcellsep 7084.970\tabcellsep 681.752\\ Total\tabcellsep \multicolumn{2}{l}{268 29560.74}\tabcellsep 29469.904\tabcellsep 1800.161\\ \multicolumn{4}{l}{RQ4. Is Facebook popularity associated with the}\\ election results?\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_6}Table 6 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{7} \par \begin{longtable}{P{0.2833333333333333\textwidth}P{0.2833333333333333\textwidth}P{0.2833333333333333\textwidth}} BJP\tabcellsep INC\tabcellsep AAP\end{longtable} \par \caption{\label{tab_7}Table 7 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{8} \par \begin{longtable}{P{0.31875\textwidth}P{0.53125\textwidth}} ANL\tabcellsep VOTES\end{longtable} \par \caption{\label{tab_8}Table 8 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{9} \par \begin{longtable}{P{0.33821052631578946\textwidth}P{0.06143859649122806\textwidth}P{0.06203508771929824\textwidth}P{0.06024561403508771\textwidth}P{0.14017543859649123\textwidth}P{0.0029824561403508773\textwidth}P{0.1354035087719298\textwidth}P{0.04950877192982456\textwidth}} \multicolumn{7}{l}{India Elections 2014: Time-Lagged Correlation between Media Bias and Facebook Trend}\tabcellsep \\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Year 2015\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 35\\ Date 24-Jan-2014 25-Jan-2014 26-Jan-2014 27-Jan-2014 28-Jan-2014 29-Jan-2014 30-Jan-2014 31-Jan-2014 01-Feb-2014 02-Feb-2014 03-Feb-2014 04-Feb-2014\tabcellsep PCONG 6 -6 5 4 44 -7 7 26 23 3 6 23\tabcellsep PBJP 13 18 5 4 9 7 8 14 22 0 27 -5\tabcellsep PAAP 5 0 15 16 4 -5 27 8 28 -2 -15 28\tabcellsep FBJP 78796 117183 45075 149679 110274 21920 67892 35194 21056 32844 99996\tabcellsep FCONG\tabcellsep FAAP 11994 18221 12368 24993 28889 8643 24926 13398 7799 10752 17483\tabcellsep Volume XV Issue II Version I\\ 05-Feb-2014\tabcellsep -2\tabcellsep 9\tabcellsep 4\tabcellsep 103576\tabcellsep \tabcellsep 13194\tabcellsep )\\ 06-Feb-2014\tabcellsep -11\tabcellsep 37\tabcellsep 12\tabcellsep 135441\tabcellsep \tabcellsep 10794\tabcellsep ( A\\ 07-Feb-2014 08-Feb-2014 09-Feb-2014 10-Feb-2014 11-Feb-2014 12-Feb-2014 13-Feb-2014 14-Feb-2014 15-Feb-2014 16-Feb-2014 17-Feb-2014 18-Feb-2014 19-Feb-2014 20-Feb-2014 21-Feb-2014 22-Feb-2014 23-Feb-2014 24-Feb-2014\tabcellsep 18 16 -14 5 18 41 9 7 -16 8 15 23 -3 22 31 15 4 12\tabcellsep 9 19 25 37 9 31 21 36 -2 -20 -15 7 13 14 4 4 14 -9\tabcellsep 4 26 26 25 -9 29 34 1 3 -2 21 15 -7 6 12 13 2 28\tabcellsep 65647 237202 70981 162104 7455 79816 19115 14545 17240 16288 12279 25315 3269 21843 29402 39720 24583 25927\tabcellsep \tabcellsep 18512 12219 12424 26964 42333 29396 19291 14701 16022 13959 6691 14915 2472 11578 26056 14376 13528 13384\tabcellsep Global Journal of Human Social Science -\\ 25-Feb-2014\tabcellsep 4\tabcellsep 19\tabcellsep 8\tabcellsep 23784\tabcellsep \tabcellsep 13802\tabcellsep \\ 26-Feb-2014\tabcellsep 18\tabcellsep 23\tabcellsep -6\tabcellsep 7287\tabcellsep \tabcellsep 4559\tabcellsep \\ 27-Feb-2014\tabcellsep -9\tabcellsep 35\tabcellsep 6\tabcellsep 24601\tabcellsep \tabcellsep 12860\tabcellsep \\ 28-Feb-2014\tabcellsep 23\tabcellsep 19\tabcellsep 23\tabcellsep 29823\tabcellsep \tabcellsep 14364\tabcellsep \\ 01-Mar-2014\tabcellsep 5\tabcellsep 18\tabcellsep 5\tabcellsep 39675\tabcellsep \tabcellsep 11853\tabcellsep \\ 02-Mar-2014\tabcellsep 4\tabcellsep 21\tabcellsep 12\tabcellsep 12952\tabcellsep \tabcellsep 15338\tabcellsep \\ 03-Mar-2014\tabcellsep 9\tabcellsep 26\tabcellsep 14\tabcellsep 24660\tabcellsep \tabcellsep 12914\tabcellsep \\ 04-Mar-2014\tabcellsep 2\tabcellsep 9\tabcellsep 8\tabcellsep 4660\tabcellsep \tabcellsep 10914\tabcellsep \\ 05-Mar-2014\tabcellsep 10\tabcellsep 16\tabcellsep 8\tabcellsep 44661\tabcellsep \tabcellsep 14915\tabcellsep \end{longtable} \par \caption{\label{tab_9}Table 9 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{10} \par \begin{longtable}{P{0.15710900473933648\textwidth}P{0.2618483412322275\textwidth}P{0.004028436018957346\textwidth}P{0.008056872037914692\textwidth}P{0.008056872037914692\textwidth}P{0.008056872037914692\textwidth}P{0.008056872037914692\textwidth}P{0.008056872037914692\textwidth}P{0.04834123222748815\textwidth}P{0.0805687203791469\textwidth}P{0.008056872037914692\textwidth}P{0.008056872037914692\textwidth}P{0.008056872037914692\textwidth}P{0.04028436018957345\textwidth}P{0.008056872037914692\textwidth}P{0.008056872037914692\textwidth}P{0.008056872037914692\textwidth}P{0.04028436018957345\textwidth}P{0.008056872037914692\textwidth}P{0.008056872037914692\textwidth}P{0.008056872037914692\textwidth}P{0.008056872037914692\textwidth}P{0.008056872037914692\textwidth}P{0.008056872037914692\textwidth}P{0.008056872037914692\textwidth}P{0.012085308056872038\textwidth}P{0.012085308056872038\textwidth}P{0.04834123222748815\textwidth}} \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Year 2015\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{8}{l}{NEWSPAPERS}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ 100\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ 50\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ 0\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ 1\tabcellsep 5\tabcellsep 9\tabcellsep 13\tabcellsep 17\tabcellsep 21\tabcellsep 25\tabcellsep 29\tabcellsep 33\tabcellsep 37\tabcellsep 41\tabcellsep 45\tabcellsep 49\tabcellsep 53\tabcellsep 57\tabcellsep 61\tabcellsep 65\tabcellsep 69\tabcellsep 73\tabcellsep 77\tabcellsep 81\tabcellsep 85\tabcellsep 89\tabcellsep 93\tabcellsep 97\tabcellsep 101\tabcellsep 105\tabcellsep 109\\ 50\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{PCONG}\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{PBJP}\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{PAAP}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{7}{l}{FACEBOOK}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ 250000\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ 200000\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ 150000\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ 100000\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ 50000\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ 0\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \tabcellsep \multicolumn{27}{l}{1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101106}\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{FCONG}\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{FBJP}\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{FAAP}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_10}Table 10 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{11} \par \begin{longtable}{P{0.2833333333333333\textwidth}P{0.2833333333333333\textwidth}P{0.2833333333333333\textwidth}} FB\tabcellsep FC\tabcellsep FA\end{longtable} \par \caption{\label{tab_11}Table 11 :}\end{figure} \footnote{© 2015 Global Journals Inc. (US)} \backmatter \begin{bibitemlist}{1} \bibitem[Singh (2014)]{b12}\label{b12} \textit{After 100 million users in India, Facebook aims at 1 billion. The Economic Times}, Shelley Singh . \url{http://articles.economictimes.indiatimes.com/2014-04-09/news/49000101\textunderscore 1\textunderscore javier-olivan-mobile-users-one-billion-users} 2014. April 9. June 25. (Published) \bibitem[Bulletins from the future The Economist (2011)]{b14}\label{b14} ‘Bulletins from the future’. \url{http://www.economist.com/node/18904136} \textit{The Economist} 2011. July 7. March 25. 2014. \bibitem[Swamy (2014)]{b13}\label{b13} \textit{Did Social Media Really Impact the Indian Elections? NDTV}, Rohan Swamy . 2014. May 20. \bibitem[Gerber et al. ()]{b5}\label{b5} \textit{Does the media matter? A field experiment measuring the effect of newspapers on voting behavior and political opinions. Yale Economic Applications and Policy Discussion}, Alan Gerber , Dean S Karlan , Daniel Bergan . 2006. (Paper 12) \bibitem[Endersby ()]{b4}\label{b4} James W Endersby . \textit{Fair and Balanced? News Media Bias and Influence on Voters. SSRN}, 2011. \bibitem[Kushin and Kitchener ()]{b8}\label{b8} ‘Getting political on social network sites: Exploring online political discourse on Facebook’. Matthew J Kushin , Kelin Kitchener . \textit{First Monday} 2009. (11) p. 14. \bibitem[Growth of Indian newspapers during recent times. E-magazine All About Newspapers (2010)]{b0}\label{b0} ‘Growth of Indian newspapers during recent times. E-magazine’. \url{http://www.allaboutnewspapers.com/sep10/article1.html} \textit{All About Newspapers} 2010. Sep-October. Retrieved March 30, 2014. 5 (5) . \bibitem[Pansare (2014)]{b10}\label{b10} ‘How social media played game changer for election 2014 in the world's largest democracy’. Prashant Pansare . \url{http://www.dnaindia.com/blogs/post-how-social-media-played-game-changer-for-election-2014-in-the-world-s-largest-democracy-1994841} \textit{DNA} 2014. June 11. June 13. (Published) \bibitem[Hayden (2012)]{b6}\label{b6} \textit{In India, English-Language Newspapers Are Thriving}, Michael E Hayden . \url{http://adage.com/article/global-news/india.english-language-newspapers-thriving/237795/} 2012. October. Retrieved March 31. 2014. (Advertising Age) \bibitem[Vitak et al. ()]{b15}\label{b15} ‘It's complicated: Facebook users' political participation in the 2008 election’. Jessica Vitak , Paul Zube , Andrew Smock , Caleb T Carr , Nicole Ellison , Cliff Lampe . \textit{Cyber Psychology, behavior, and social networking}, 2011. 14 p. . \bibitem[Chiang and Knight ()]{b2}\label{b2} ‘Media Bias and Influence: Evidence from Newspaper Endorsements’. Chun-Fang Chiang , Brian Knight . \url{http://restud.Oxfordjournals.org/content/78/3/795.short} \textit{Review of Economic Studies} 2011. 78 (3) p. . \bibitem[Bartels ()]{b1}\label{b1} ‘Messages received: the political impact of media exposure’. Larry M Bartels . \textit{American Political Science} 1993. 87 (2) p. . \bibitem[Kuypers ()]{b9}\label{b9} \textit{Press bias and politics: How the media frame controversial issues}, Jim A Kuypers . 2002. Greenwood Publishing Group. \bibitem[Rhee and Woong ()]{b11}\label{b11} ‘Strategy and issue frames in election campaign coverage: A social cognitive account of framing effects’. June Rhee , Woong . \textit{Journal of Communication} 1997. 47 (3) p. . \bibitem[Woolley et al. ()]{b17}\label{b17} \textit{The 2008 presidential election, 2.0: A content analysis of user-generated political Facebook}, Julia K Woolley , M Anthony , Mary Beth Limperos , Oliver . 2010. \bibitem[Druckman and Parkin ()]{b3}\label{b3} ‘The impact of media bias: How editorial slant affects voters’. James N Druckman , Michael Parkin . \textit{Journal of Politics} 2005. 67 (4) p. . \bibitem[Hooke (2012)]{b7}\label{b7} \textit{Why newspaper markets are growing in China and India, while they decline in the US and UK. eJournalist. Retrieved}, Paolo Hooke . \url{http://ejournalist.com.au/v12n1/Hooke.pdf} 2012. March 30, 2014. \bibitem[Williams and Gulati ()]{b16}\label{b16} Christine B Williams , Girish J Gulati . \textit{Social networks in political campaigns: Facebook and the 2006 midterm elections. Annual meeting of the American Political Science Association}, 2007. \end{bibitemlist} \end{document}