\documentclass[12pt, letterpaper]{article}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{amsthm}
\usepackage{graphicx}
\usepackage{graphics}
\usepackage{rotating}
\usepackage{setspace}
\usepackage{fullpage}
\usepackage{placeins}
\usepackage{multirow}
\usepackage{floatrow}
\usepackage{cite}
\usepackage{dcolumn}
\usepackage{float}
\usepackage{booktabs,caption}
\usepackage{tablefootnote}
\usepackage{lscape} 
\usepackage{tabularx}
\usepackage{epigraph}
\usepackage[flushleft]{threeparttable}
\usepackage{fontspec}
\usepackage{tikz}
\def\checkmark{\tikz\fill[scale=0.4](0,.35) -- (.25,0) -- (1,.7) -- (.25,.15) -- cycle;}
\usepackage{hyphenat}
\usepackage[position=top]{caption}
\setlength{\parindent}{4em}
\usepackage{subcaption}
\usepackage{longtable}
\usepackage{natbib} 
\usepackage[titletoc,title]{appendix}
\usepackage{footnote}
\makesavenoteenv{tabular}
\usepackage{authblk}
\usepackage{floatrow}
\usepackage{caption}
\usepackage{subcaption}
\usepackage{fontspec}
\floatsetup[table]{capposition=top}
\floatsetup[figure]{capposition=top}
\usepackage[colorlinks=true, linkcolor = black, citecolor = blue, urlcolor=orange, pdfborder={0 0 0}]{hyperref}

\title{Appendix \\ Panem et Circenses: Removing Political News to Generate Electoral Support, Evidence from Berlusconi's Italy}
\author[1]{Andrea De Angelis\thanks{Corresponding author: deangelis@ipz.uzh.ch}}
\author[2]{Alessandro Vecchiato}

\affil[1]{Department of Political Science, University of Zurich, Affolternstrasse 56, 8050 Zurich, Switzerland \\  
Email: deangelis@ipz.uzh.ch; 
ORCID ID: 0000-0001-6866-6683} 
\affil[2]{Cyber Policy Center, 616 Jane Stanford Way, Stanford University, Stanford, CA 94305 \\ 
Email: vecchiato@stanford.edu; ORCID ID: 0009-0001-0320-9032} 
\date{20 December 2023}


\begin{document}

\maketitle

%---------------------------------   APPENDICES ------------------------------------
%-----------------------------------------------------------------------------------

\clearpage

\tableofcontents

\appendix

\clearpage
% Appendix A

\section{Data Preprocessing}
\label{app:prep}
Using news transcripts as data for the scaling model required several preprocessing steps.
Text analysis with \texttt{WORDFISH}, as in general with ``bag of words'' approaches, involves converting the corpus of raw documents into a document-feature matrix of discrete occurrences of all the tokens in the documents. To this end, we relied on the \href{https://cran.r-project.org/web/packages/tm/tm.pdf}{$tm$ R package}.

The creation of the data-feature matrix proceeds as follows:
\begin{enumerate}
\item We merge all the news stories' text by day for each TV channel.
This effectively shifts the unit of analysis from the single news story to the [TV channel $\times$ day] level.
\item Removal of punctuation using conventional tools from $tm$\footnote{Such as the $removePunctuation()$ function} would behave stripping off the apostrophe and linking the leading character to the previous one\footnote{This works on English text corpora because apostrophes indicate possession and are thus always following the word, as in ``\textit{the professor's hat}''. The punctuation removal would result in ``\textit{professors hat}'', and once the words are stemmed the additional ``s'' character would be removed. In the Italian language, apostrophes are often associated with the article's elision when the following word starts with a vowel. Thus, the punctuation removal of ``\textit{l'amica}'' [the female friend] and ``\textit{un'amica}'' [a female friend] would wrongly result in two different tokens: ``\textit{lamica}'' and ``\textit{unamica}'', and this would be unaffected by the following stemming process.}. We thus preliminarily apply a regular expression to substitute all punctuation characters with a single space character.
\item We transform all text into lower case characters.
\item We remove stopwords in the text\footnote{We have created a custom Italian stopwords list composed as the union of 1) all the \href{http://snowball.tartarus.org/algorithms/italian/stop.txt}{$snowball$} stopwords list that is typically included in the most common R packages (as in $tm$ and $quanteda$); 2) all the words contained in the \href{http://www.ranks.nl/stopwords/italian}{$Ranks\ NL$} stopwords list; 3) the following list of 34 additional stopwords chosen after visual inspection: \texttt{\{l, poi, far, quest, qual, tant, quel, dic, so, quell, avev, piu, fa, vorrebb, gia, puo, s, sar, d, nun, ce, n, foss, x, b, va, ogni, vuol, andar, propr, fatt, vann,  www, fonte\}.}}.
\item We remove words of one and two letters.
\item We remove all numbers.
\item We stem the document using the \href{https://tartarus.org/martin/PorterStemmer/}{Porter's stemming algorithm}.
\item We create the document-feature matrix, including all unigrams in the text corpus.
\item We remove the top $5\%$ and the bottom $5\%$ tokens by frequency\footnote{This is a standard procedure in quantitative text analysis that aims at cutting non-informative long tails in the distribution of words.}.
\item We filter out documents with less than 20 tokens (evaluated to be too short and thus uselessly making the algorithm's convergence difficult).
\end{enumerate}

The data covers the period from the week starting on the 01 of June 2011, until the week starting on the 30 of April 2012.

\newpage
\section{Descriptive Statistics}
\renewcommand{\thetable}{B.\arabic{table}} % Change table numbering to "B.x"
\setcounter{table}{0} % Reset table counter for Appendix B



\begin{table}[!ht]
\begin{center} \small
\caption{Descriptive Statistics of TV Newscasts}
\label{tab:summary}
\begin{tabular}{lllllll} 
\hline\hline 
\multicolumn{1}{l}{Newscast}&\multicolumn{1}{l}{Network}&\multicolumn{1}{l}{Ownership}&\multicolumn{1}{l}{Broadcasts}&\multicolumn{1}{l}{Segments} \\
\multicolumn{1}{l}{}&\multicolumn{1}{c}{}&\multicolumn{1}{c}{}&\multicolumn{1}{l}{Time}&\multicolumn{1}{c}{}  \\
\multicolumn{1}{l}{}&\multicolumn{1}{c}{}&\multicolumn{1}{c}{}&\multicolumn{1}{l}{}&\multicolumn{1}{c}{} \\
\hline

Studio Aperto	&   Italia 1  	&Mediaset& 12:30 	& 26619 \\
 			&  			&		& 18:30 	& 23149  \\
			&  			&		& 		& 		 \\
\hline
TG1			&   Rai 1  		&RAI 	& 08:30 	& 17463	 \\
 			&  			&		& 13:30	& 22383	 \\
			&  			&		& 17:00	& 313	 \\
			&  			&		&  20:00	& 27203	 \\
			&  			&		& 		& 		 \\
\hline
TG2			&   Rai 2  		&RAI 	& 13:00 	& 22840	 \\
 			&  			&		& 20:30	& 18649	 \\
			&  			&		& 		& 		 \\
\hline
TG3			&   Rai 3  		&RAI 	& 12:00 	& 14433	 \\
 			&  			&		& 14:30	& 12340	 \\
			&  			&		& 19:00	& 18324	 \\
			&  			&		& 		& 		 \\
\hline
TG4			&   Rete 4 	&Mediaset& 11:30 	& 20016	 \\
 			&  			&		& 13:30	& 994	 \\
			&  			&		& 19:00	& 29688	 \\
			&  			&		& 		& 		 \\
\hline
TG5			&   Canale 5 	&Mediaset& 08:30 	& 28123	 \\
 			&  			&		& 13:00	& 27062	 \\
			&  			&		& 20:00	& 25961	 \\
			&  			&		& 		& 		 \\
\hline
TG7			&   la7	 	&Telecom & 07:30 	& 15454	 \\
 			&  			&		& 12:30	& 27062	 \\
			&  			&		& 13:30	& 25961	 \\
			&  			&		& 20:00	& 19422    \\
			&  			&		& 		& 		 \\
\hline
\hline
\multicolumn{7}{p{11cm}}{\small Broadcast times are in hh:mm (24h) format. Segments refers to a single news story broadcast in the corresponding time slot. Segments numbers are total frequency over the entire 2010-2014 period. Segments exclude direct quotes from politicians.} \\
\end{tabular}
\end{center}
\end{table}

\clearpage

\begin{table}[!ht] \centering
\begin{threeparttable}
  \caption{Descriptive Statistics of ITANES data}
  \label{tab:sample}
  \begin{tabular}{@{\extracolsep{5pt}}lccccc}
  \\[-1.8ex]\hline \hline\\[-1.8ex]
  Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Max} \\
  \hline \\[-1.8ex]
  Age & 3,343 & 49.006 & 17.291 & 18 & 98 \\
  Favourite Newscast (Tg) - Wave 1 & 3,222 & 4.239 & 2.393 & 1 & 8 \\
  Favourite Newscast (Tg) - Wave 2 & 3,215 & 4.388 & 2.442 & 1 & 8 \\
  Favourite Newscast (Tg) - Wave 3 & 2,764 & 4.376 & 2.440 & 1 & 8 \\
  Frequency watching favorite newscast - Wave 1 & 3,330 & 2.680 & 0.711 & 0 & 3 \\
  Frequency watching favorite newscast - Wave 2 & 3,324 & 2.644 & 0.748 & 0 & 3 \\
  Frequency watching favorite newscast - Wave 3 & 2,841 & 2.681 & 0.700 & 0 & 3 \\
  Propensity To Vote for PDL - Wave 1 & 3,111 & 2.997 & 3.632 & 0 & 10 \\
  Propensity To Vote for PDL - Wave 2 & 3,117 & 2.600 & 3.321 & 0 & 10 \\
  Propensity To Vote for PDL - Wave 3 & 2,720 & 2.157 & 2.974 & 0 & 10 \\
  Likeability of Berlusconi - Wave 1 & 3,182 & 3.363 & 3.265 & 0 & 10 \\
  Likeability of Berlusconi - Wave 2 & 3,225 & 2.749 & 2.957 & 0 & 10 \\
  Likeability of Berlusconi - Wave 3 & 2,752 & 2.452 & 2.677 & 0 & 10 \\
  Left-right self-positioning - Wave 1 & 3,011 & 4.854 & 2.976 & 0 & 10 \\
  Left-right self-positioning - Wave 2 & 2,999 & 4.702 & 2.927 & 0 & 10 \\
  Left-right self-positioning - Wave 3 & 2,479 & 4.511 & 2.707 & 0 & 10 \\
  Ideological distance with PDL - Wave 1 & 2,799 & 3.918 & 3.196 & 0 & 10 \\
  Ideological distance with PDL - Wave 2 & 2,796 & 4.012 & 3.209 & 0 & 10 \\
  Ideological distance with PDL - Wave 3 & 2,349 & 4.012 & 3.034 & 0 & 10 \\
  Overall government evaluation - Wave 1 & 3,236 & 3.215 & 2.869 & 0 & 10 \\
  Overall government evaluation - Wave 2 & 3,274 & 3.021 & 2.562 & 0 & 10 \\
  Overall government evaluation - Wave 3 & 3,272 & 3.344 & 2.506 & 0 & 10 \\
  \hline\hline \\[-1.8ex]
  \end{tabular}
  \begin{tablenotes}
  \item The table reports the descriptive statistics, including mean and standard deviation, for all the dependent and independent variables used in Table 6.
  \end{tablenotes}
  \end{threeparttable}
\end{table}

The ITANES 2011-2013 panel data consisted of five waves of mixed CATI-CAWI interviews. Overall, N=1,159 respondents have completed all five waves. The waves were fielded in the following dates: 

I WAVE: CATI 9 February-3 May 2011; CAWI 17 January – 22 February 2011

II WAVE: CATI 3 October -3 novembre 2011; CAWI 5 October-31 October 2011

III WAVE: CATI 18 May -26 June 2012; CAWI 7 May – 5 June 2012

IV WAVE: CATI 7 January-9 February 2013; CAWI 7 January – 31 January 2013

V WAVE: CATI 4-27 March 2013; CAWI 27 February – 28 March 2013. 


\end{document}