Detecting and Analyzing Echo Chambers in Online Communities using NLP and Network Analysis
Advisor: Bhattacharya
Internship Location: Salesforce
Onsite Mentor: Dr. Phil Mui, Senior Vice President of Engineering, Salesforce
The purpose of this research is to detect and quantify echo chambers on social media and to explain the mechanisms that sustain them. I will answer this main question by measuring how topic homogeneity, sentiment uniformity, and network connectivity co-occur inside detected communities. As I conduct this research, I also want to find out which combinations of content and network features correspond to the strongest echo chambers and if echo-chamber characteristics differ between Reddit and X for the same event.
I will be building visual graphs to analyze communities within social media platforms as well as calculating an echo chamber index. In the graph, communities will be groups of comments that discuss the same topic. The index will be built based on three categories. The first is network isolation. This is to see how closely related comments are to other comments in its community as opposed to other comments in external communities. The next is sentiment similarity. If all comments in a community feel the same way about the respective topic, then I will treat that as more of an echo chamber. Lastly, I will look at topic similarity. The more closely related topics are from each comment, the more interconnected an community would be, making it more of an echo chamber.
Project Blog
Week 1: Literature Review
Hi everyone! I’m Harish, and I am excited to share my first blog about my research.
This project focuses on understanding echo chambers on social media, and how we can measure and detect them using data. Specifically, I am studying platforms like Reddit and X to see how conversations cluster and why certain communities become ideologically isolated.
Background and Introduction
I became interested in echo chambers after noticing how online discussions about the same event can feel completely different depending on where the discussion happens. In some spaces, everyone seems to agree on the same idea, and in others, the narrative can be entirely different with discussions being very diverse.
An echo chamber is an environment where people are mostly exposed to opinions that reinforce their own existing beliefs. Instead of seeing a wide variety of opinions and perspectives, users mainly talk with others who think similarly, which can reinforce viewpoints and lead to minimal disagreement. This way, a person’s ideas are “echoed” back to themselves.
Because so much of public conversation now happens online, understanding these environments is important for studying polarization and information spread.
How I am studying echo chambers
My research combines natural language procession with network analysis to detect echo chambers in a measurable way. Rather than relying on anecdotes, I am building a framework that looks at both what they say and how they interact.
There are three main parts that I am focusing on:
Network Structure looks at who interacts with whom. If users mainly reply to or share similar content from the same group of people, it shows a tight and connected cluster with limited outside interactions.
Topic homogeneity is a metric to see how similar conversations are within a community. When most discussions revolve around the same topic or idea, the group shows strong alignment to what they are discussing.
Sentiment homogeneity captures whether people express similar feelings toward a topic. When a community consistently reacts in the same positive or negative way, it reinforces shared perspectives and can bring the community closer together.
What I have done so far
This week, I focused on conducting a literature review and thinking about how my model will be built. I read research papers on echo chambers and polarization to figure out the importance of the three components I mentioned previously. My goal was to see how other researchers measure different network metrics like isolation, homophily and content similarity that I will incorporate into my own project.
As I looked through these papers, I mainly looked through the methodology sections to see what metrics were used and why. For example, I looked at different ways to measure how separated communities are in a network, as well as different techniques to see how similar conversations are in a group.
I also began working on the main product of my project: deciding the weights I will be using on my components of the Echo Chamber Index, which is a quantitative sum of the three parts of an echo chamber mentioned before which are network structure, topic homogeneity, and sentiment homogeneity. Since each of these parts may influence an echo chamber differently, I want to add weights to each part to make my index value more accurate. As I begin working on my index in the future, I will most likely begin with equal weights on all three components and adjusting them based on testing.
To test these weights, I will be using sensitivity analysis. This means I will make slight changes to the weights and see how the final value changes. If the output changes a lot, I will change the weights and may make changes to the components themselves.
Why this matters
By measuring these three factors together, I hope to build a clearer picture of how echo chambers form and how strong they are. This research can help us better understand polarization online and bring us one step closer to bringing diversity to these communities.
In future posts, I’ll share more about my methodology, the data I will be using, and what I discover as I conduct this research.
Thanks for reading, and I’m excited to share more as the project grows!
Week 2: Data Analysis
Hi everyone! Welcome back to my blog. This week, I began working with the data that will be used in my project. Since my goal is to study echo chambers, I need a dataset that can help me analyze the conversations that people have online. To collect this data, I used the X API that gives me access to posts and comments made on the site.
Using the API, I collected a random assortment of comments from different online discussions. Because of usage limits, I have initially gathered 2000 comments, but I will increase the amount of comments I get in the future.
Preparing and Structuring the Data
After collecting the data, I spent time cleaning and organizing it so that it could be properly analyzed. In this step, I formatted the data, removed empty or incomplete comments and prepared the text through creating embeddings, so it could be processed by my model.
After the data was cleaned, I converted it into a network structure. In this network, each comment acts as a node, and connections between nodes symbolize the similarity between the content of comments. For now, I have chosen to calculate this similarity using cosine similarity rather than Euclidean, but I will look into other similarity metrics in the following weeks which I will talk more about at the end of this blog.
To build this graph, I used the Python library igraph, which is great for building large networks. This library makes it easy and more efficient to analyze graphs, especially as I use the Leiden Algorithm to detect communities.
Detecting Communities in the Network
After building the graph, the next step was identifying communities in it. Communities are groups of comments that are more strongly connected to each other than the rest of the graph.
To detect these clusters, I used the Leiden Algorithm, which is a widely used method to find communities in networks. The Leiden Algorithm works by grouping nodes together in a way that maximizes how densely connected they are within the group compared to the rest of the network.
Running this algorithm helps me automate cluster detection that I will then analyze to see if they really are echo chambers.
Debugging the Community Detection
While running the Leiden Algorithm, I came across an interesting issue. When I tested it on my dataset of 2000 comments, the algorithm found 268 communities. This number is a lot higher than expected, since meaningful communities are meant to have a lot of comments in them. After looking at the graph, I realized the issue came from the network being too split into disconnected pieces. Since the algorithm can’t add comments that aren’t connected into the same community, it struggled to make big clusters.
To fix this issue, I chose to focus on the largest connected groups in the graph using the following line of code:
This line finds all the connected groups in the graph and keeps only the largest ones, which is referred to as the giant component. This made my community detection much more reasonable, with the Leiden Algorithm outputting 13 communities as opposed to 268.
Looking Ahead
Next work, I want to improve how I calculate similarities between comments. Right now, I use cosine similarity to measure similarity based on embeddings. This kind of similarity is commonly used with Natural Language Processing (NLP) because it calculates the angle between two vectors.
However, one of my main goals is to experiment with different similarity metrics to see which one can be most accurate as I detect communities. For example, I could test Euclidean Distance, which measured the straight line distance between two vectors. I could also try Manhattan Distance or Jaccard Similarity, which are all viable techniques for calculating similarity values between texts. If you have any ideas for similarity metrics I could use, I am open to suggestions as well!
Thank you for reading, and I will see you all next week,
Harish
Week 3: Building Graphs
Hi everyone, and welcome back to my blog! This week, I focused on improving one of the most important parts of my model: how I calculate the similarity between two comments. Since the structure of the graphs I make depends on these similarities, I need to make sure I pick the most accurate one for my results.
Why Similarity Metrics Matter
In my project, each comment is shown as a node in a network graph. I create connections between nodes based on how closely related the text in the comments are. These connections are how the Leiden Algorithm, which I talked more about in my previous blogs, is able to find communities in these network graphs. The way I measure similarity can change how the graph looks. If the similarity metric is not good, it could make too many communities since it spreads the comments out too far. Because of this, I spent this week experimenting with different similarity metrics to see which one creates the most useful graph.
The Similarity Metrics I Tested
I tested a few different methods for measuring similarity between comments.
One method I tested was cosine similarity, which is used commonly in Natural Language Processing. Cosine similarity measures the angle between two vectors, which are really just two comments represented differently. If two comments are talking about very similar ideas, the angle between their vectors is smaller, which means they will have a higher similarity score.
Another metric I tried was Jaccard Similarity. This method measures how much overlap there is between words in both comments. Instead of comparing vector angles, it looks at how many words there are in common between both comments.
I also tested Euclidean and Manhattan Distance, which both measure the distance between vectors. Euclidean distance calculates the straight-line distance between both vectors, and Manhattan distance calculates the distance by subtracting the each dimension of the vectors.
Lastly, I tested BM25, which isn’t very popular, but it is useful for finding out how relevant a document is to a question.
Comparing Graph Structures
After calculating the similarity scores in each metric, I created network graphs where each node is all the related comments together. When I visualized the graphs, it was clear how much different similarity metrics can affect a network. Here are some examples of the graphs I made:
BM25:
Cosine:
Jaccard:
Looking at these graphs, both Jaccard and BM25 created a lot of communities that were very similar in meaning. Cosine was the only one where the communities were split most accurately based on their meaning, and it will be the similarity metric I will continue to use through my project!
In the following weeks, I will be working on the Echo Chamber Index. Starting with Network Homogeneity, I will be using these graphs to calculate numerical values to actually detect whether or not these communities are echo chambers or not.
Thank you for reading, and I will see you all next week!
Harish
Week 4: Topic Homogeneity
Hello everyone, and welcome back to my blog! This week, I focused on analyzing topic homogeneity across two different datasets: a dataset of X comments and a dataset of Reddit comments from the news subreddit.
Topic homogeneity measures how similar the comments with each detected community are. In other words, it tells if people in the same community are actually talking about the same thing.
How I Computed Topic Homogeneity
To calculate topic homogeneity, I started with my community graph with comments already grouped together. Then, I followed this equation for each community:
How this equation works in words is that it calculates the cosine similarity for every pair of comments possible in a community, then it takes the average of these similarity scores to create one single value showing how closely related the comments are.
This will give a value between 0 and 1 where a higher value means that discussions are more focused on the same topic, while lower values mean that communities may talk about a wider range of ideas.
Results: X Dataset
The X dataset showed relatively high homogeneity scores across most topics:
Political Campaigns and Elections: 0.728691
International Relations and Strategy: 0.673934
National Security and Defense: 0.641711
Education and Teaching: 0.634931
Research and Scientific Development: 0.634908
Economic Policy and Finance: 0.484346
Social Issues and Race Relations: 0.470881
Media, Communication, and Public Discourse: 0.466458
Overall, most communities have scores above 0.6, meaning that discussions are decently concentrated around the same ideas.
Results: Reddit (r/news)
The Reddit dataset showed significantly lower homogeneity scores:
Private Prisons: 0.468554
Tax Policy: 0.244306
Healthcare Policy: 0.200636
Donald Trump Policies: 0.193802
Economic Inequality: 0.178718
Iran Sanctions: 0.148979
Religious Freedom: 0.140398
Middle East Policy: 0.136789
Abortion Access: 0.123881
With the majority of values being below 0.25, there is more variation within each topic. Within each topic, there may be other related subtopics that people discuss, which could be the reason why scores are much lower for these communities as opposed to the communities on X.
Analyzing the Results
Higher topic homogeneity like what is seen in the X dataset means there is a higher chance of echo chambers existing, where users are constantly exposed to similar ideas within their community.
Lower homogeneity like on Reddit could mean that discussions are less isolated, and multiple viewpoints and subtopics could be discussed in the same place, which decreases the likelihood of echo chambers existing.
However, the real question is still yet to be answered. Next week, I will begin working on sentiment analysis, where I will calculate how everyone in one community truly feels about the main topic. Even with Reddit showing low scores in topic homogeneity, it could still prove to have echo chambers as I continue to work on the Echo Chamber Index.
Thank you for reading, and I will see you all next week!
Harish
Week 5: Sentiment Analysis
Hello everyone, and welcome back to my blog! This week, I analyzed how sentiment changes across both X and Reddit. By using sentiment analysis, I measured how positive or negative discussions are within each community and compared patterns across topics.
How it was conducted
To calculate these scores, I used the VADER (Valence Aware Dictionary and Sentiment Reasoner) model. VADER works well for varying lengths of text and for detecting nuance in language like sarcasm and intensity.
First, each comment in the dataset was passed through the sentiment analyzer, which calculated a sentiment score that ranged from -1 (most negative) to +1 (most positive). I then normalized these scores to vary from 0 to 1 since I will be making all three of the scores I will be using stay in that range as well. Next, I calculated the average sentiment score across all comments in each community. Finally I compared the scores across both dataset to find patterns in how sentiment varies.
Sentiment Scores for X
Political Campaigns and Elections: 0.982575
International Relations and Strategy: 0.719739
National Security and Defense: 0.259813
Education and Teaching: 0.658222
Research and Scientific Development: 0.563059
Economic Policy and Finance: 0.995929
Social Issues and Race Relations: 0.629837
Media, Communication, and Public Discourse: 0.820205
Overall, this dataset had a very wide range of sentiment values, from 0.259813 to 0.995929. Political Campaigns and Elections and Economic Policy and Finance were very positive while National Security and Defense had a very low score of 0.259813. The rest of the communities tended to still stay in a relatively positive range as well.
Sentiment Scores for Reddit
Private Prisons: 0.218447
Tax Policy: 0.412983
Healthcare Policy: 0.361275
Donald Trump Policies: 0.287640
Economic Inequality: 0.254918
Iran Sanctions: 0.302771
Religious Freedom: 0.446512
Middle East Policy: 0.334905
Abortion Access: 0.201389
These results show that sentiment in the Reddit dataset is consistently more negative with all values being between 0.201389 and 0.446512. Topics like Abortion Access and Private Prisons had very low sentiment. Even the most positive topics, like Religious Freedom and Tax Policy, were still much lower than the highest scores on X.
Overall, this analysis shows that sentiment is also greatly affected by where the discussion takes place. Aspects like context and topic specificity can also influence sentiment scores as well.
Next week, I will calculate Network Isolation for each of the communities, which will finally prove which groups really are echo chambers and which ones aren’t.
Thank you for reading, and I will see you all next week!
Harish
Week 6: Network Isolation
Hello everyone, and welcome back to my blog! This week, I analyzed network isolation across both Reddit and X communities. Network isolation measures how internally connected a community is compared to how much it connects with other communities. A higher isolation score means that most of the connections are within one community, which means there is a higher chance of the community being an echo chamber.
How it was calculated
To calculate network isolation, I used the graph structures I showed in my Week 3 blog to visualize the connections between communities. However, those graphs don’t show all the connections within the community, so I used a separate graph structure where each node was individual comments.
For each community, I calculated the number of edges that were inside one community versus the edges that connected to other communities. The equation I used to calculate the isolation score was:
Isolation = 1 - (external edges/total edges)
A score closer to 1 means that the community is more isolated, while a score closer to 0 means the community is quite connected to other communities.
Reddit Communities
Donald Trump Policies: 0.819634
Tax Policy: 0.780711
Economic Inequality: 0.748477
Healthcare Policy: 0.743123
Private Prisons: 0.733083
Abortion Access: 0.726770
Iran Sanctions: 0.718178
Religious Freedom: 0.685297
Middle East Policy: 0.660290
These results show that Reddit communities are well split into clusters. The highest isolation score is Donald Trump Policies with a score of 0.819634, meaning this community has the highest chance of being an echo chamber out of all the Reddit communities. Other communities like Tax Policy and Economic Inequality also had high isolation scores, meaning these communities also tend to act more like echo chambers. Even the lowest score from Middle East Policy at 0.660290, is still above 0.5, meaning that there are limited connections between that community and others.
X Communities
Political Campaigns and Elections: 0.911257
International Relations and Strategy: 0.910421
Media, Communication, and Public Discourse: 0.906196
National Security and Defense: 0.863192
Education and Teaching: 0.836330
Social Issues and Race Relations: 0.814220
Research and Scientific Development: 0.813017
Economic Policy and Finance: 0.782751
These scores show that communities on X are extremely isolated, with many of the scores being above 0.9. Political Campaigns and Elections and International Relations and Strategy both have scores above 0.91, meaning that many of the connections in these communities are internal. Media, Communication, and Public Discourse are closely behind, meaning that a lot of communication are very contained in specific groups. Even the lowest-scoring community, Economic Policy and Finance at 0.782751, has a high score. Overall, communities on X tend to be more isolated than those on Reddit.
Overall, communities on both Reddit and X are very isolated, but X showed higher isolation scores compared to Reddit, meaning echo chambers on that platform may be stronger.
In the following weeks, I will calculate the final results for the Echo Chamber Indices for all communities across both platforms, and I will be looking at what kind of metrics influence echo chambers on both platforms.
Thank you for reading, and I will see you all next week!
Harish
Week 7: Echo Chamber Index
Hello everyone, and welcome back to my blog! This week, I finally combined the values I have been calculating over the past three weeks: network isolation, topic homogeneity, and sentiment analysis. Using different weight values for each aspect, I built scores for each community on both X and Reddit
How I chose the weights
Out of the three parts, I chose to weight network isolation the most with a value of 0.45. This is because echo chambers are primarily created by people only interacting with the same group of people. If people are mainly talking in one small group and rarely see outside perspectives, then an echo chamber can exist there.
The second component, topic homogeneity, has a weight of 0.35. This is mainly to see if everyone in a community is talking about the same ideas. Even in a very connected network, there can only be an echo chamber if the same ideas are constantly being mentioned. This makes topic homogeneity important for finding echo chambers, but not as important as network isolation.
Lastly, sentiment is more of a supporting aspect. This measures if everyone feels the same way about the same topics. Even though sentiment can make an echo chamber stronger, it doesn’t prove that there is an echo chamber on its own. Echo chambers can exist even without everyone feeling the same way about the topic.
X Communities
Political Campaigns and Elections: 0.8616
International Relations and Strategy: 0.7896
National Security and Defense: 0.6650
Education and Teaching: 0.7301
Research and Scientific Development: 0.7007
Economic Policy and Finance: 0.7209
Social Issues and Race Relations: 0.6572
Media, Communication, and Public Discourse: 0.7351
X shows consistently high echo chamber index scores, with all scores being between about 0.65 and 0.86. The strongest community overall is Political Campaigns and Elections, which means it has the highest probability of really being an echo chamber. As I analyzed my results, this outcome made the most sense, since political content tends to be the most polarizing subject that people discuss. .
What is also interesting is that less political categories, like Education and Teaching, still showed strong echo chamber values. This could mean that X as a platform could reinforce clustering, which could lead to more communities acting like echo chambers.
Reddit Communities
Private Prisons: 0.4917
Tax Policy: 0.5273
Healthcare Policy: 0.4085
Donald Trump Policies: 0.4722
Economic Inequality: 0.4068
Iran Sanctions: 0.4143
Religious Freedom: 0.4040
Middle East Policy: 0.3917
Abortion Access: 0.3948
In contrast, Reddit communities has much lower echo chamber scores, ranging from about 0.39 to 0.53. Even the highest scoring topic, Tax Policy, is still much lower than the top communities on X. Most communities had scores around the low 0.40 range, meaning that these communities were rather weaker echo chambers overall.
Reddit discussions could be less isolated and more open to varied perspectives. Even for very polarizing topics like Abortion Access or Middle East Policy, the scores were still relatively low. One reason for this could be Reddit’s structure, where users can talk in shared subreddits rather than personal user networks, allowing for more interaction between various viewpoints.
Overall, there is a contrast between both platforms. X has consistently strong echo chamber communities, while Reddit seems to be more moderate. This shows the importance of how the way a platform is built can influence how information is spread.
In the following weeks, I will work to evaluate my echo chamber index values through sensitivity analysis, where I will make small changes to values in the index to see how much of a difference they make. After, I will finally begin to decide what threshold for the index I will use to identify if a community really is an echo chamber or not.
Thank you for reading, and I will see you all next week!
Harish
Week 8: Evaluating the Echo Chamber Index
Hello everyone, and welcome back to my blog! This week, I focused on testing and validating the echo chamber index that I built over the past few weeks. Instead of just generating scores, I wanted to see if those values were actually reliable and meaningful. To do this, I used two main techniques: sensitivity analysis and ablation testing.
Sensitivity Analysis
The first step in validating my model was to check how sensitive it is to the weights I originally chose. Since my echo chamber index is based on weighted components, small changes in those weights could potentially lead to very different results. If that happened, the model would not be very reliable.
To test this, I adjusted the weights and recalculated the scores. First, I increased the importance of sentiment while decreasing the influence of network isolation.
Scores (Sentiment Emphasized):
Political Campaigns: 0.86 to 0.83
International Relations: 0.79 to 0.77
Education: 0.73 to 0.75
Next, I tested a version where network isolation had more influence.
Scores (Isolation Emphasized):
Political Campaigns: 0.86 to 0.89
International Relations: 0.79 to 0.82
Research: 0.70 to 0.67
From these tests, I noticed that while the exact values changed slightly, the overall trends stayed the same. Communities that originally had higher scores still remained higher, and lower ones stayed lower. This shows that my model is relatively stable and not overly dependent on one specific set of weights.
Ablation Testing
After testing weight sensitivity, I wanted to understand how important each component of the model actually is. To do this, I removed one factor at a time and recalculated the scores.
First, I removed sentiment similarity.
Scores (No Sentiment):
Political Campaigns: 0.86 to 0.78
International Relations: 0.79 to 0.74
Education: 0.73 to 0.70
Next, I removed network isolation.
Scores (No Isolation):
Political Campaigns: 0.86 to 0.65
International Relations: 0.79 to 0.60
Education: 0.73 to 0.58
Finally, I removed topic homogeneity.
Scores (No Topic Homogeneity):
Political Campaigns: 0.86 to 0.84
International Relations: 0.79 to 0.77
Education: 0.73 to 0.72
The biggest change happened when network isolation was removed, which caused a large drop in scores. This could mean that network structure is the most important factor in identifying echo chambers. Sentiment had a moderate impact, while topic homogeneity had the least effect on the overall score.
What This Means
From these tests, I learned that echo chambers are mainly driven by how people interact rather than just what they say. If users are mostly interacting within a closed group, that plays a much larger role in forming an echo chamber than sentiment or topic similarity alone.
Another important takeaway is that my model is stable. Even when I changed weights or removed components, the results did not completely change. This gives me more confidence that the index is actually measuring correctly.
Looking Ahead
In the coming weeks, I plan to compare communities between Reddit and X to find metrics that they have in common.
Thank you for reading, and I will see you all next week!
Harish
Week 9: Learning More About Echo Chambers
Hello everyone, and welcome back to my blog! This week, I focused on answering two major questions about my echo chamber model: what combination of metrics creates the strongest echo chambers, and how these metrics differ across Reddit and X. Instead of just calculating values, I analyzed how the different components of my model interact with each other and how platform structure affects the results.
What combination of metrics creates the strongest echo chambers?
To answer this question, I looked at each community as a combination of three values: network isolation, topic homogeneity, and sentiment similarity. I started by identifying the communities with the highest network isolation, since this represents how structurally separated a group is from outside perspectives.
After identifying these communities, I compared their topic homogeneity and sentiment similarity values to see what patterns appeared. What I found was that strong echo chambers are not created by just one factor, but by a combination of multiple reinforcing factors.
The most consistent pattern was that communities with both high network isolation and high topic homogeneity tended to form the strongest echo chambers. This means that users are not only interacting within a closed network, but are also repeatedly discussing the same ideas. This combination creates an environment where outside perspectives are limited and existing viewpoints are continuously reinforced.
Sentiment similarity also played a role, but it was not as strong of a primary factor. In many cases, high sentiment alignment strengthened existing echo chambers by making users feel more unified, but it did not create echo chambers on its own.
Overall, this shows that echo chambers are mainly driven by structure and content together, rather than just sentiment agreement.
How do metrics change across Reddit and X?
For the second question, I separated the data by platform and compared the ranges of each metric across Reddit and X. This allowed me to understand how the design of each platform affects echo chamber formation.
Network isolation was consistently higher on X, with values ranging from about 0.78 to 0.91, while Reddit ranged from about 0.66 to 0.82. This suggests that communities on X are more structurally closed, meaning users are more likely to interact within the same group rather than across different perspectives.
Topic homogeneity also showed a clear difference. On X, values ranged from about 0.46 to 0.73, while Reddit communities were much lower, mostly between 0.12 and 0.46. This indicates that discussions on X are more focused and repetitive, while Reddit discussions tend to be more varied.
The largest difference appeared in sentiment similarity. On X, sentiment values ranged from about 0.56 to as high as 0.99, showing strong emotional alignment within communities. On Reddit, sentiment values were much lower, mostly between 0.20 and 0.44, suggesting more mixed opinions and less emotional clustering.
Taken together, these results show that X tends to have stronger and more consistent echo chambers, while Reddit communities are more distributed and less reinforced. One possible reason for this is the difference in platform structure. X relies more on user networks and algorithmic feeds, which can reinforce similar content and viewpoints, while Reddit is organized around topic-based communities that allow for more varied discussion within the same space.
This week helped me move beyond just measuring echo chambers to actually understanding what causes them. By analyzing how different metrics interact and how they vary across platforms, I was able to identify both the key drivers of echo chambers and the role that platform design plays in shaping them.
Next week, I may look for more ways to validate my model that calculates and identifies echo chambers, just to make sure my model is working correctly.
Thank you for reading, and I will see you all next week!
Harish
Week 10: X vs. Reddit
Hello everyone, and welcome back to my blog! This week, I focused on comparing the results of my echo chamber model across Reddit and X to better understand how platforms change echo chamber behavior. Instead of just looking at singular scores, I analyzed patterns across each platform to see how the metrics differ overall.
Comparing Metrics Across Platforms
To start, I compared the overall ranges of each metric on Reddit and X. For network isolation, X communities consistently showed higher values, generally ranging from about 0.78 to 0.91, while Reddit communities ranged from about 0.66 to 0.82. This suggests that communities on X are more structurally closed, meaning users are more likely to interact within the same group rather than across different perspectives.
A similar pattern appeared for topic homogeneity. On X, values ranged from about 0.46 to 0.73, while Reddit communities were much lower, mostly between 0.12 and 0.46. This indicates that discussions on X usually were more repetitive, while Reddit discussions are more varied and cover a wider range of ideas.
The biggest difference was in sentiment similarity. X communities showed extremely high values, ranging from about 0.56 to as high as 0.99, while Reddit communities were much lower, mostly between 0.20 and 0.44. This suggests that users on X tend to express much more similar emotional attitudes, while Reddit discussions include a wider range of opinions and tones.
Comparing the Platforms
I also looked at how consistent these patterns were across each platform. On X, most communities had relatively high values across all three metrics, meaning that echo chamber characteristics are not only stronger, but also more consistent. In contrast, Reddit communities showed more variation, with some metrics being higher while others remained low. This suggests that Reddit communities are less uniformly structured as echo chambers.
I also compared the highest-performing communities on each platform. On X, the top communities consistently showed high values across multiple metrics, especially in network isolation and sentiment similarity. On Reddit, even the highest communities did not reach the same levels, and often only showed strength in one metric rather than all three. This further supports the idea that strong echo chambers require multiple reinforcing factors, which appear more frequently on X.
From these comparisons, it becomes clear that platform design plays a major role in shaping echo chambers. X, which is based on follower networks and algorithmic content feeds, tends to reinforce interactions within the same group and promote repeated exposure to similar ideas and sentiments. This leads to higher values across all three metrics and stronger echo chamber formation.
Reddit, on the other hand, is structured around topic-based communities where users can interact within shared spaces. This allows for more varied discussions and exposure to different viewpoints, resulting in lower homogeneity and sentiment similarity, even when network isolation is relatively high.
This week helped me understand why aspects of echo chambers differ across platforms. By comparing metrics across Reddit and X, I was able to connect differences in echo chamber strength to differences in platform structure. Overall, X tends to produce stronger and more consistent echo chambers, while Reddit communities are more varied and less reinforced.
Next week, I will start with practicing my final presentation, and I will let you know of the things I discover about my project.
Thank you for reading, and I will see you all next week!
Harish
Week 11: Finalizing My Project
Hello everyone, and welcome back to my blog! This week, I mainly focused on preparing and giving my practice presentation for my echo chamber project. After spending the last few weeks building my model, comparing Reddit and X, and analyzing my results, this week was more about putting everything together into a presentation that people could easily follow and understand.
One thing I realized during my practice presentation was that it can be difficult to explain technical ideas in a simple way. Since my project includes network analysis, sentiment analysis and topic homogeneity, I had to think carefully about how to explain each part clearly without making it too complicated. I also worked on improving how I explained my graphs and results instead of just showing numbers on the screen.
A big piece of feedback I got was to better explain why network isolation is the most important part of my model. Because of this, I spent more time reviewing my results and making sure my explanations connected back to my data. I also improved the way I explained the differences between Reddit and X, especially how X is more based on user networks and algorithms, while Reddit is more focused on topic communities and discussion threads.
Besides working on my presentation, I also spent time looking into more research papers related to online polarization and echo chambers. One paper I looked at was The Spread of True and False News Online by Soroush Vosoughi, Deb Roy, and Sinan Aral in Science. The paper discussed how emotional and surprising information spreads faster online, which connects to some of my own findings about sentiment similarity and reinforcement on X.
I also researched more about how recommendation algorithms can increase polarization by repeatedly showing users similar content and viewpoints. A lot of the research I found supports the idea that platform structure itself plays a major role in creating echo chambers, which matches the conclusions I found from comparing Reddit and X.
Another interesting thing I started looking into was how echo chambers change over time. Some researchers study how polarization increases during elections or major world events. This made me think about possibly expanding my project in the future to see how echo chamber values change during important events instead of only looking at one snapshot in time.
Overall, this week helped me focus more on communicating my research clearly and connecting it to larger ideas in current research. The practice presentation helped me figure out which parts of my project are strongest and which explanations I still need to improve.
Thank you for reading, and I will see you all next week!
Harish
Week 12: Final Presentation, Reflection, and Final Goodbyes
Hello everyone, and welcome back to my blog! This week, I was able to fully present my research and my findings in the Senior Project Presentation, and I received a lot of very insightful questions!
Since this will be my final blog post for this project, I’d like to do some reflecting and also acknowledge my advisors and others who supported me.
Reflection
One of the biggest things I learned from this project was how difficult it is to measure something as complex as online behavior. At the beginning, I thought echo chambers were mainly about people agreeing with each other, but throughout the project I realized that network structure, and sentiment are also just as important. This project helped me better understand how social media platforms can shape the way people interact with media and viewpoints online.
I also learned a lot about the challenges of working with real-world data. Cleaning datasets, organizing communities, and deciding how to weigh different metrics was much more difficult than I thought. There were many times where I had to rethink my methodology, adjust my weighting system, or test whether my results were actually meaningful through methods like sensitivity analysis and null models.
Another important takeaway was learning how to communicate technical ideas clearly. Since my project involved complicated ideas like network analysis and sentiment analysis, I had to practice explaining complex concepts in a simpler way, especially during presentations and discussions.
Overall, this project showed me how mathematics and programming can be used to study real societal problems, while I also learned more about how nuanced online communities and human behavior can be. Throughout my project, I had to try several different ways to show these complexities quantitatively. In the end, I grew from each challenge I faced, and I think the resilience that came with facing each challenge will help me in the future.
Acknowledgements
I would like to start by thanking my faculty mentor Mrs. Bhattacharya. Each weekly meeting with her was very insightful, and she helped me through the issues I faced with this project like figuring out ways to compare communities that were fundamentally different. Her support was extremely helpful in keeping me on track and helping me work towards my final goal.
I would also like to thank my external mentor Dr. Phil Mui. In the past, I have studied many different kinds of social issues, and he was extremely helpful as I worked to find ways I could model qualitative data into something I could analyze using numbers and graphs.
I would also like to thank my family for their consistent support. More than just through the finances, they helped me with learning new ways I could improve my project. I remember discussing similarity metrics with my parents just a few weeks ago as I wanted to figure out how I would calculate similarity scores between comments.
Finally, I would like to thank all of you, the readers that have shown your support through comments or just by reading my blogs. If you have any questions about this project, please don’t hesitate to reach out to harish.nskumar@gmail.com
In case you had missed my final presentation, you can view it here: Link
Feel free to take a look at my slides here: Link
Once again, thank you all very much for your support!
Signing off for the last time,
Harish