Sol Messing
@solmg.bsky.social
Social Scientist/Research Prof at NYU CSMaP, formerly Twitter. http://solomonmg.github.io
7/ Kernel Approximation Ideal Point Poisson Factorization - scalable Bayesian ideological estimation from billions of observations, applied to 134 million TikTok comments to map ideology on the platform. @matiaspiqueras.bsky.social
Matías Piqueras (@matiaspiqueras.bsky.social)
PhD student in Computer Science at Uppsala University working on developing computer vision models and methods relevant to the study of politics and society.
matiaspiqueras.bsky.social
September 11, 2025 at 6:26 PM
7/ Kernel Approximation Ideal Point Poisson Factorization - scalable Bayesian ideological estimation from billions of observations, applied to 134 million TikTok comments to map ideology on the platform. @matiaspiqueras.bsky.social
6/ Propaganda or Parity? testing whether TikTok amplifies pro-China content, using LLM classifiers and longitudinal engagement/moderation data. @kengchichang.bsky.social, @mollyeroberts.bsky.social, H Barnehl
September 11, 2025 at 6:26 PM
6/ Propaganda or Parity? testing whether TikTok amplifies pro-China content, using LLM classifiers and longitudinal engagement/moderation data. @kengchichang.bsky.social, @mollyeroberts.bsky.social, H Barnehl
5/ To Be or Not to Be on TikTok: a rare activation experiment, recruiting users to start TikTok and measuring causal effects on attitudes, knowledge and well-being.
K Rutherford, @tiagoventura.bsky.social
K Rutherford, @tiagoventura.bsky.social
September 11, 2025 at 6:26 PM
5/ To Be or Not to Be on TikTok: a rare activation experiment, recruiting users to start TikTok and measuring causal effects on attitudes, knowledge and well-being.
K Rutherford, @tiagoventura.bsky.social
K Rutherford, @tiagoventura.bsky.social
4/ Scrolling Through Hate: mapping hate speech on TikTok across time, place, topic, plus experiments testing moderation responsiveness.
responsiveness.
@karstendonnay.bsky.social, @fabriziogilardi.bsky.social , @gloriagennaro.bsky.social, @dhangartner.bsky.social
responsiveness.
@karstendonnay.bsky.social, @fabriziogilardi.bsky.social , @gloriagennaro.bsky.social, @dhangartner.bsky.social
September 11, 2025 at 6:26 PM
4/ Scrolling Through Hate: mapping hate speech on TikTok across time, place, topic, plus experiments testing moderation responsiveness.
responsiveness.
@karstendonnay.bsky.social, @fabriziogilardi.bsky.social , @gloriagennaro.bsky.social, @dhangartner.bsky.social
responsiveness.
@karstendonnay.bsky.social, @fabriziogilardi.bsky.social , @gloriagennaro.bsky.social, @dhangartner.bsky.social
3/ First: The Political Supply of TikTok: political content spreads faster than entertainment, and a small set of creators dominates reach. @benguinaudeau.bsky.social, K Rutherford, @jatucker.bsky.social
September 11, 2025 at 6:26 PM
3/ First: The Political Supply of TikTok: political content spreads faster than entertainment, and a small set of creators dominates reach. @benguinaudeau.bsky.social, K Rutherford, @jatucker.bsky.social
2/ Short-form video platforms (TikTok, Reels, Shorts) are reshaping political comms: vertical video, personalized feeds, huge reach. But opaque data access makes them hard to study.
September 11, 2025 at 6:26 PM
2/ Short-form video platforms (TikTok, Reels, Shorts) are reshaping political comms: vertical video, personalized feeds, huge reach. But opaque data access makes them hard to study.
Reposting @hwaight.bsky.social ‘s thread on this bsky.app/profile/hwai...
I am thrilled to share a new article in Sociological Methods & Research, “Quantifying Narrative Similarity Across Languages”. My co-first author Sol Messing and our collaborators developed a new approach to measuring “narrative similarity” between texts: journals.sagepub.com/doi/10.1177/...
Quantifying Narrative Similarity Across Languages - Hannah Waight, Solomon Messing, Anton Shirikov, Margaret E. Roberts, Jonathan Nagler, Jason Greenfield, Megan A. Brown, Kevin Aslett, Joshua A. Tuck...
How can one understand the spread of ideas across text data? This is a key measurement problem in sociological inquiry, from the study of how interest groups sh...
journals.sagepub.com
June 21, 2025 at 12:21 PM
Reposting @hwaight.bsky.social ‘s thread on this bsky.app/profile/hwai...
Here's a link to the paper! solomonmg.github.io/pdf/Quantify...
solomonmg.github.io
June 20, 2025 at 2:20 PM
Here's a link to the paper! solomonmg.github.io/pdf/Quantify...
Limitations exist—measuring narrative similarity doesn't alone prove "diffusion." Contextual and temporal analyses remain essential for robust conclusions about propaganda or any information dynamics.
June 20, 2025 at 2:20 PM
Limitations exist—measuring narrative similarity doesn't alone prove "diffusion." Contextual and temporal analyses remain essential for robust conclusions about propaganda or any information dynamics.
Authorship Analysis:
Current methods often employ BERT for authorship attribution. However, larger, modern LLMs (like GPT-4o) remain under-explored for this task. There's untapped potential here waiting to be studied.
Current methods often employ BERT for authorship attribution. However, larger, modern LLMs (like GPT-4o) remain under-explored for this task. There's untapped potential here waiting to be studied.
June 20, 2025 at 2:20 PM
Authorship Analysis:
Current methods often employ BERT for authorship attribution. However, larger, modern LLMs (like GPT-4o) remain under-explored for this task. There's untapped potential here waiting to be studied.
Current methods often employ BERT for authorship attribution. However, larger, modern LLMs (like GPT-4o) remain under-explored for this task. There's untapped potential here waiting to be studied.
Science of Science:
Tracking idea origins in scientific literature traditionally uses topic models or exact text reuse, often missing important conceptual linkages. Our method could clarify how ideas propagate through academic communities.
Tracking idea origins in scientific literature traditionally uses topic models or exact text reuse, often missing important conceptual linkages. Our method could clarify how ideas propagate through academic communities.
June 20, 2025 at 2:20 PM
Science of Science:
Tracking idea origins in scientific literature traditionally uses topic models or exact text reuse, often missing important conceptual linkages. Our method could clarify how ideas propagate through academic communities.
Tracking idea origins in scientific literature traditionally uses topic models or exact text reuse, often missing important conceptual linkages. Our method could clarify how ideas propagate through academic communities.
Information Reuse:
The study of content recycling ("memetracking") relies heavily on exact text matches. Using our approach could identify deeper connections—tracing the subtle evolution and spread of ideas.
The study of content recycling ("memetracking") relies heavily on exact text matches. Using our approach could identify deeper connections—tracing the subtle evolution and spread of ideas.
June 20, 2025 at 2:20 PM
Information Reuse:
The study of content recycling ("memetracking") relies heavily on exact text matches. Using our approach could identify deeper connections—tracing the subtle evolution and spread of ideas.
The study of content recycling ("memetracking") relies heavily on exact text matches. Using our approach could identify deeper connections—tracing the subtle evolution and spread of ideas.
Our method has potential beyond narrative similarity. Here are some potential applications:
Plagiarism Detection:
Exact-text matching often misses subtle, paraphrased copying. Our approach could vastly improve recall, catching nuanced cases traditional methods miss.
Plagiarism Detection:
Exact-text matching often misses subtle, paraphrased copying. Our approach could vastly improve recall, catching nuanced cases traditional methods miss.
June 20, 2025 at 2:20 PM
Our method has potential beyond narrative similarity. Here are some potential applications:
Plagiarism Detection:
Exact-text matching often misses subtle, paraphrased copying. Our approach could vastly improve recall, catching nuanced cases traditional methods miss.
Plagiarism Detection:
Exact-text matching often misses subtle, paraphrased copying. Our approach could vastly improve recall, catching nuanced cases traditional methods miss.
And here's the same story appearing shortly after and appearing in Infowars
June 20, 2025 at 2:20 PM
And here's the same story appearing shortly after and appearing in Infowars
What does this look like in practice? Here's an article in Sputnik alleging a false-flag operation by the US:
June 20, 2025 at 2:20 PM
What does this look like in practice? Here's an article in Sputnik alleging a false-flag operation by the US:
Now it's important to note that often matching narratives represent humdrum coverage of the same real-world developments:
June 20, 2025 at 2:20 PM
Now it's important to note that often matching narratives represent humdrum coverage of the same real-world developments:
And here's what we actually found: low quality news outlets with lower journalistic standards are more likely to print narratives appearing in Russian state media outlets
June 20, 2025 at 2:20 PM
And here's what we actually found: low quality news outlets with lower journalistic standards are more likely to print narratives appearing in Russian state media outlets
We use purposive sampling at various decision boundaries to oversample positive cases to generate labeled training and validation data sets. This allows us to estimate recall and thus F1!
June 20, 2025 at 2:20 PM
We use purposive sampling at various decision boundaries to oversample positive cases to generate labeled training and validation data sets. This allows us to estimate recall and thus F1!