I’m writing a 10-page research paper on How are Political Campaigns Utilizing Algorithms on Political Polarized Content and the Formation of Echo Chambers. The research question/topic is “How did Trump’s 2024 Instagram Posts Utilizing Algorithms on Political Polarized Content and the Formation of Echo Chambers.”
I have written the first five pages of the paper (which is the ‘Literature Review’ document). It should be the first section of the paper. I have attached all the cited sources in the literature review to this order so you can continue using them if you want to, but you don’t have to as I have already used them in the Literature Review (they have ‘lit review source’ in their file name). Please change the Introduction and Conclusion of it, adding the research part on Trump’s post.
I have also done the research on Trump’s Instagram posts (which is in the ‘Research On Trump’s Instagram Posts’ document). You can use those papers/documents directly as they are written by me for this paper, (no need to paraphrase, unless needed).
For the files with ‘[source]’ in the file name are additional sources you may include (use at least two of them), please let me know which one you decide to use.
Here are some possible directions for you to take into consideration:
The research shows that certain posts, particularly those with abundant interaction (high likes or comments), heavily feature specific keywords like “Trump,” “Thank,” “America,” “MAGA,” and “Wins.” These keywords are not only indicative of the content’s focus but also reflect a particular political narrative and emotional tone.
Algorithmic Bias: Social media algorithms are designed to show users content that aligns with their preferences and beliefs. This personalized content delivery can create echo chambers where users are exposed mainly to content that reinforces their existing views. My observation of Instagram posts reflecting a specific political viewpoint resonates with this aspect of algorithmic bias.
Emotional Engagement: Socially responsive algorithms often prioritize content that elicits strong emotional reactions, such as joy or fear. The research mentions the use of emotionally charged content in the form of sarcastic memes or parodic videos targeting Biden. This aligns with the idea that emotional stimuli can shape attitudes and behaviors, contributing to polarization.
Information Filtering: Algorithms not only prioritize content based on user preferences but also filter out content that doesn’t align with those preferences. This can lead to a limited diversity of information and reinforce existing biases. My observation that users receive minimally diverse content and form echo chambers supports this notion of information filtering.
Behavioral Conditioning: Social media algorithms engage in behavioral prediction based on user data. By showing users content that aligns with their beliefs, preferences, and emotional responses, these algorithms condition users to react in certain ways. My data analysis on the prevalence of specific keywords and themes in highly interacted posts reflects this conditioning effect.
By drawing these connections, you can argue that socially responsive algorithms on platforms like Instagram contribute to political polarization by reinforcing existing beliefs, limiting information diversity, and shaping user behavior and attitudes. This alignment strengthens the argument that algorithmic influence plays a significant role in contemporary political discourse and polarization.
You can draw your personal connection to reach a better argument if you can come up with something.
