Youtube and the myth of Political Neutrality
- The political color of Youtube -
American electoral politics have been dominated by two major political parties: the Democratic Party and the Republican Party. While democrats are liberal and left-leaning, republicans adopted a conservative right-leaning ideology. Each year, the world watches how these two powerful parties dispute power and pick sides. In this context, does Youtube have a political preference? Does it pick a side of its own?
To answer this, we try to understand if and how YouTube aligns with the political phenomenon in the US. Through this analysis, we aim to shed light on the role that YouTube plays in shaping political views and the potential for bias on the platform.
YouTube is the world's largest video hosting service. In the recent years, Youtube audience grew more than 10 times: from 200 millions in 2010 to 2.5 billions in 2021. Such platform with a large user base has the potential to shape public discourse and influence the way people think about certain issues. If it is perceived as biased, it may only amplify certain viewpoints and silence others, leading to a distorted public discourse. Political bias can contribute to the polarization of society, as people are exposed to more one-sided views and may be less likely to engage with viewpoints that differ from their own.
Let's all listen to Donald Trump telling us all about the dangers of the Social Media Platform. Isn't it ironic that this is the exact tool he used to win the 2016 elections?
Thanks to YouNiverse dataset, we have access to youtube's content throughout the years. It contains a rich database of 72.9 millions videos, 136 thousands channels and 8.6 billions comment metadata we perform a deep dive into the topic.
First, we filter channels that have political content and are based in US, since the discussion is relevant for the US population. We perform a similar filtering on Youtube videos, only taken into account US videos from the News & Politics category.
We end up with 2,285,761 political videos and 8160 channels, after enriching our data with the help of Youtube Data API.
Using many different data analysis techniques (like real Data Scientists!) and external datasets, we are diving deep into the political world of YouTube.
For political affiliation labelling we trained a political-flavoured BERT model on Democrat Vs. Republican Tweets. We particularly used this dataset because we wanted our model to understand how people are expressing political ideas on social platforms (people talk differently online, aren't they?). We split the dataset into 80% train and 20% validation, obtaining an accuracy of 94.4%, so our model is pretty impressive.
Our model then analyses the concatenation between title and description to inpher wheter the video has Democratic orientation or Republican.
In order to deepen our understanding about the political phenomenom, we use a sentiment analysis model to label the sentiment of a video using videos' description and title.
In order to correctly label the setiment of each video we trained our political-flavoured BERT model on Twitter and Reddit Sentimental analysis Dataset. We used this dataset since we wanted to capture how people are expressing their emotions on social platforms.
Nowadays, everything is a continuous run for attention and affirmation, and Youtube is the right place for it. With a huge influence on the society, Youtube might be a powerful persuasive tool, which seems to make it the right one for politicians. Youtube's content reaches to hundreds and thousands of people every year. Thus, the more content a political party has the better chances to influence the audience has.
Which party has a better influence based on the above affirmation?
From the above plot we cannot conclude that throughout the years there is an overwhealming difference in the number of videos from both political spectrums. However, we can observe that there is a slight tendency towards more republican content posted each month. Since the quantity is our first metric, we can say that republicans have more influence, but this is only the starting point. However, we are not led by the premise that quantity is over quality, because Youtube is not about tons of content, but about likes and views.
In the pursuit of likes(votes), everyone would do anything. So do politicians.
Even though there is more republican content, democrats have the power to gather definitely more engagement after Trump announces his candidacy for US president elections in June 2015, as we can observe from the likes. However, the pattern also can be observerd from the views. For this reason, we can conclude that quantity is not enough.
How open are politicians to technology? How well do they understand the power of social platforms and use them it within the larger context of their campaigns?
We analysed the extent of YouTube use as an election tool across parties for both House of Representatives and Senate candidates. We searched wheter or not each candidate has an official Youtube channel (we only considered Youtube verified accounts to be valid) at the time of the election.
When comparing the percentage of how many candidates within a party have a Youtube channel at the moment of the election, we can see that Democrats have a higher percentage of politicians with Youtube channel than Republicans. This stands true for every year between 2008 and 2018, for both Senate and House of Representatives elections.
We have seen that some politicians choose to use Youtube as an election tool while others don't. We take it one step further and try to predict the 2018 elections results for the Senate based on the popularity of the candidate Youtube Channel.
For each state, we check which one of the candidates has more youtube subscribers one week before the election. We predict that this politician will be the winner of the election. However, if the politician don't both have a Youtube account, we predict as the winner the politician that has a Youtube channel. If neither of the candidates have a valid Youtube channel, we don't have enough data to make a prediction.