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* Don't make mobile PageLink style global * Add a Methods page * Add team picture * Actually add team photo * No more leet names * More vertical padding, bluer dark foreground * Make body thinner
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@@ -8,9 +8,10 @@ export const CONTACT_NAME = "Terra Oh"; | |
export const CONTACT_EMAIL = "[email protected]"; | ||
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export const PAGES = [ | ||
{ link: "/about", name: "About", leetName: "AB0UT" }, | ||
{ link: "/timeline", name: "Timeline", leetName: "T1M3L1N3" }, | ||
{ link: "/blog", name: "Blog", leetName: "B10G" }, | ||
{ link: "/about", name: "About", leetName: "ABOUT" }, | ||
{ link: "/methods", name: "Methods", leetName: "METHODS" }, | ||
{ link: "/timeline", name: "Timeline", leetName: "TIMELINE" }, | ||
{ link: "/blog", name: "Blog", leetName: "BLOG" }, | ||
] as const; | ||
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interface SocialMediaLink { | ||
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--- | ||
title: Methods | ||
layout: ../layouts/BaseLayout.astro | ||
--- | ||
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# Methods | ||
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We will be analyzing comments from Reddit, a popular social media platform. | ||
Reddit has a sizable, though not particularly diverse, user base, a significant portion | ||
of which is human. Additionally, it has data stretching back to 2007, all of which can | ||
be accessed for free. We will request data from 2010 to 2023. | ||
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Once we collect our data, we will pick some commonly used mental health related words to study, | ||
such as "trauma" and "depression." Then, we will conduct three different analyses on it. | ||
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The first will be a quali-quantitative co-word analysis, which will help us gain an understanding of the semantic | ||
change in the words we are studying. | ||
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The second will be a quantitative analysis based on Word2vec embeddings of the words we are studying. | ||
We will slice up the time range under study. Within each time slice, we will generate | ||
Word2vec embeddings of the words under study. These embeddings will be generated in a way | ||
that embeddings for the same word from two different time slices can be compared. | ||
The comparison will be simple cosine similarity between vectors, and will allow us to quantify | ||
semantic change in each word between any two time slices. | ||
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The third will be a quali-quantiative analysis based on BERT embeddings of the words we are studying. | ||
For each of the words under study, we will cluster all of its embeddings within each time slice. | ||
Each cluster should represent a different sense of the word, allowing us to track how each | ||
individual sense of the word has changed over time. | ||
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Since this would be entirely unsupervised, we need to ensure that humans are in the loop. | ||
Threfore, we will then manually check that these clusters make sense and regenerate them | ||
using different hyperparamets if necessary. | ||
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Finally, we will put together all of the information we have gleaned through our different | ||
analyses to determine how the terms we are studying have changed over time. |
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