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\title{Data Mining CS 571}
\author{Matt Jensen}
\date{2023-04-25}

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\maketitle

\section*{Abstract}

News organizations have been repeatedly accused of being partisan.
Additionally, they have been accused of polarizing dicussion to drive up revenue and engagement.
This paper seeks to quantify those claims by classifying the degree to which news headlines have become more emotionally charged of time.
A secondary goal is the investigate whether news organization have been uniformly polarized, or if one pole has been 'moving' more rapidly away from the 'middle'.
This analysis will probe to what degree has the \href{https://en.wikipedia.org/wiki/Overton_window}{Overton Window} has shifted in the media.
Naom Chomsky had a hypothesis about manufactured consent that is beyond the scope of this paper, so we will restrict our analysis to the presence of agenda instead of the cause of it.


\begin{multicols}{2}

\section{Background}

There is evidence supporting and increase in political polarization in the United States over the past 16 years.
There have been a number of studies conducted in an attempt to measure and explain this phenomenon. \cite{stewart_polarization_2020} \cite{flaxman_filter_2016}

These studies attempt to link increased media options and a decrease in the proportion of less engaged and less partisan voters. 
This drop in less engaged voters might explain the increased partisanship in elections.
However, the evidence regarding a direct causal relationship between partisan media messages and changes in attitudes or behaviors is inconclusive.
Directly measuring the casual relationship between media messages and behavior is difficult \cite{prior_media_2013}.
There is currently no solid evidence to support the claim that partisan media outlets are causing average Americans to become more partisan.

The number of media publishers has increased and in this particular data set:
    
\begin{Figure}
    \centering
    \includegraphics[width=\linewidth]{figures/distinct_publishers.png}
    \captionof{figure}{Publishers Per Year}
\end{Figure}

These studies rest on the assumption that media outlets are becoming more partisan.
We study this assumption in detail.

Party Sorting: Over the past few decades, there has been a significant increase in party sorting, where Democrats have become more ideologically liberal, and Republicans have become more ideologically conservative. This trend indicates a growing gap between the two major political parties. A study published in the journal American Political Science Review in 2018 found that party sorting increased significantly between 2004 and 2016.

Congressional Polarization: There has been a substantial increase in polarization among members of the U.S. Congress. Studies analyzing voting patterns and ideological positions of legislators have consistently shown a widening gap between Democrats and Republicans. The Pew Research Center reported that the median Democrat and the median Republican in Congress have become further apart ideologically between 2004 and 2017.

Public Opinion: Surveys and polls also provide evidence of increasing political polarization among the American public. According to a study conducted by Pew Research Center in 2017, the gap between Republicans and Democrats on key policy issues, such as immigration, the environment, and social issues, has widened significantly since 1994.

Media Fragmentation: The rise of social media and digital media platforms has contributed to the fragmentation of media consumption, leading to the creation of ideological echo chambers. Individuals are more likely to consume news and information that aligns with their pre-existing beliefs, reinforcing and intensifying polarization.

    Increased Negative Attitudes: Studies have shown that Americans' attitudes towards members of the opposing political party have become increasingly negative. The Pew Research Center reported in 2016 that negative feelings towards the opposing party have doubled since the late 1990s, indicating a deepening divide.

\section{Data Preparation}
The subject of analysis is a set of news article headlines scraped from the news aggregation site \href{https://mememorandum.com}{Memeorandum} for news stories from 2006 to 2022.
    Each news article has a title, author, description, publisher, publish date, url and related discussions \ref{tab:1}. 
The site also has a concept of references, where a main, popular story may be covered by other sources.
This link association might be used to support one or more of the hypothesis of the main analysis.
After scraping the site, the data will need to be deduplicated and normalized to minimize storage costs and processing errors.

What remains after these cleaning steps is approximitely 6,400 days of material, 300,000 distinct headlines from 21,000 publishers and 34,000 authors used in the study.

\begin{center}
    \begin{tabular}{ll}
        publishers & 1,735     \\
        stories    & 242,343   \\
        children   & 808,628   \\
        date range & 2006-2022
    \end{tabular}
\captionof{table}{dataset statistics}
\label{tab:1}
\end{center}


\section{Missing Data Policy}

The largest data policy that will have to be dealt with is news organizations that share the same parent company, but might have slightly different names.
Wall Street Journal news is drastically different than their opinion section. 
Other organizations have slightly different names for the same thing and a product of the aggregation service and not due to any real difference.
Luckily, most of the anaylsis is operating on the content of the news headlines, which do not suffer from this data impurity.

\section{Classification Task}

The classification of news titles into emotional categories was accomplished by using a pretrained large langauge model from \href{https://huggingface.co/arpanghoshal/EmoRoBERTa}{HuggingFace}.
This model was trained on \href{https://ai.googleblog.com/2021/10/goemotions-dataset-for-fine-grained.html}{a dataset curated and published by Google} which manually classified a collection of 58,000 comments into 28 emotions.
The classes for each article will be derived by tokenizing the title and running the model over the tokens, then grabbing the largest probabilty class from the output.

The data has been discretized into years.
    Additionally, the publishers will have been discretized based of either principle component analysis on link similarity or based on the bias ratings of \href{https://www.allsides.com/media-bias/ratings}{All Sides}.
Given that the features of the dataset are sparse, it is not expected to have any useless attributes, unless the original hypothesis of a temporal trend proving to be false.
Of the features used in the analysis, there are enough data points that null or missing values can safely be excluded.

\section{Experiments}

No computational experiment have been done yet.
Generating the tokenized text, the word embedding and the emotional sentiment analysis have made up the bulk of the work thus far.
The bias ratings do not cover all publisher in the dataset, so the number of articles without a bias rating from their publisher will have to be calculated.
If it is less than 30\% of the articles, it might not make sense to use the bias ratings.
The creation and reduction of the link graph with principle component analysis will need to be done to visualize the relationship between related publishers.

\section{Results}

\begin{Figure}
    \centering
    \includegraphics[width=\linewidth]{figures/articles_per_year.png}
    \captionof{figure}{Three simple graphs}
\end{Figure}

test

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