Love Island Language Model (LiLM): Exploring Reddit Sentiment and Predicting Kalshi Odds
Love Island is introduced every episode as “the most talked-about dating show in America.” With all the drama that comes with putting attractive, young singles into isolation and recording it for all of us to see, discussions thrived all over social media this summer. Did you see what Sincere did last night? Do you agree with how the Islanders reacted? Love Island Redditors actively discussed topics like these throughout the season
This year, Kalshi prediction markets got in on the action, bringing a completely new demographic into the Love Island ecosystem. A traditionally female-dominated viewership suddenly overlapped with sports bettors. That meant the same fans placing wagers on a Knicks and Spurs playoff game were now hedging bets on Brinity versus Carniya.
My analysis revealed a strong association between Reddit sentiment and an Islander’s Kalshi price. A one-standard-deviation positive increase in an Islander’s sentiment correlated to a +5.5¢ bump in their Kalshi win probability. While I hold back strict claims of causality, the correlation is clear: discussion fuels the market.
Viz 1: Reddit Sentiment vs. Kalshi Betting Price
An interactive visualization can be accessed on Github pages.
While Trinity maintained consistently positive sentiment and Bryce fluctuated across relative sentiment z-scores, the divergence in Kalshi pricing shows that the betting market decisively chose its favorite around Episode 19.
An interactive Plotly visualization tracking the relationship between an Islander’s Reddit sentiment and Kalshi market odds over time available at: https://palautatan.github.io/data-by-edie/004-love-island/viz/network/sentiment_vs_kalshi.html.
Viz 2: Network Analysis over Time
Episode-per-episode network visualization is available here.
The network visualization illustrates distinct sub-communities formed around couples and close friendships, showing that while pairs like Kayda and Zach were frequently discussed together, co-occurrence did not translate to the positive reception enjoyed by couples like Brinity.
An interactive visualization tracking the evolution of Islander co-mentions across the season available on Github pages. Upon hovering, you can see the number of islander co-mentions and sentiment metrics. Link: https://palautatan.github.io/data-by-edie/004-love-island/viz/network/co_mention_network.html.
Q&A
How did you gather the data? The Reddit data was gathered using a Python scraping tool to pull episode-specific discussion threads from the start of the season until the end. Kalshi data were simply downloaded directly off of Kalshi. I used DuckDB as my backend engine to process and join the datasets.
What sentiment analysis engines did you use? In Python, I used the Twitter-RoBERTa machine learning model to extract sentiment and toxicity scores on Islander-centered text snippets to accurately tie the Reddit comments to the correct person. I also used LLMs to gauge overall audience approval, keeping only data from Season 8.
What are the takeaways? It is odd, you know? Betting on a contest where the audience is also able to vote. It’s like screaming into a vacuum a little bit. While I can’t claim we can perfectly predict winners from Reddit sentiment, I ran an OLS linear regression (since Kalshi prices are a 0–100 continuous scale) that revealed something fascinating. Controlling for the episode timeline, Reddit sentiment was highly predictive of Kalshi odds.
The Vibe is Priced In: A one-standard-deviation increase in an Islander’s positive sentiment correlated to a +5.5¢ increase in their Kalshi win probability.
Mo’ Discussed, Mo’ Odds: Total discussion volume mattered. Just keeping your name in the daily conversation pushed odds up.
No Villain Penalty: Highly toxic Reddit threads about an Islander did not statistically lower their odds of winning. The market ignores pure internet vitriol as long as the Islander remains relevant.
Reddit absolutely loved Trinity Tatum! Sentiment surrounding Trinity was mostly above average, while her boyfriend Bryce’s sentiment varied over the season.
What are the shortcomings?
The Data: Due to API limits, I could not retrieve every single discussion thread. I also treated nested replies as standalone comments, flattening the conversational context.
The NLP Approach: Sentiment was extracted using windowed text snippets centered around an Islander’s name. Strictly speaking, this measures the emotional tone of the words immediately surrounding a person rather than a direct, syntactic evaluation of the Islander themselves.
The Regression: The model is an exploratory lens, not a production forecaster. Our regression R² was 0.085. We could have used a better model to explain these data.
