While news outlets are certainly drivers of fatigue, readers are not entirely off the hook. Research shows that negative headlines have more than a 60 percent higher click-through rate than positive ones—à la the old trope, “if it bleeds, it leads.”

I always feel that there are way more bad news than good news until now. I made a tally of the posts on the homepage of Beehaw right now and registered 14 as positive, 10 as negative, and 15 as neutral wrt my stance. It just seems like I actively focus more on the bad ones. Maybe I will try reading more positive ones.

I’m assuming lemmy’s bug is acting up again lol. Anyway, I am also very excited about Sync.

Surveys After each song, participants were asked to rank how much they liked the song (1 to 10), if they would replay the song (0, 1), recommend the song to their friends (0, 1), if they had heard it previously to assess familiarity (0, 1), and if they found the song offensive (0, 1). We also showed participants lyrics from the song and lyrics created by the researchers and asked them to identify the song lyrics to measure their memory of the song (0, 1).

I still think your concern is legitimate.

Abstract

Identifying hit songs is notoriously difficult. Traditionally, song elements have been measured from large databases to identify the lyrical aspects of hits. We took a different methodological approach, measuring neurophysiologic responses to a set of songs provided by a streaming music service that identified hits and flops. We compared several statistical approaches to examine the predictive accuracy of each technique. A linear statistical model using two neural measures identified hits with 69% accuracy. Then, we created a synthetic set data and applied ensemble machine learning to capture inherent non-linearities in neural data. This model classified hit songs with 97% accuracy. Applying machine learning to the neural response to 1st min of songs accurately classified hits 82% of the time showing that the brain rapidly identifies hit music. Our results demonstrate that applying machine learning to neural data can substantially increase classification accuracy for difficult to predict market outcomes.

So they use synthetic data to both train and test their model, this is because the original dataset contains only 24 songs.

Next, we assessed the bagged ML model’s ability to predict hits from the original 24 song data set. The bagged ML model accurately classified songs with 95.8% which is significantly better than the baseline 54% frequency (Success = 23, N = 24, p < 0.001).

So the 97.2% accuracy is reported on the synthetic data. On the original one, it is 95.8%. But the authors do acknowledge the limitations.

While the accuracy of the present study was quite high, there are several limitations that should be addressed in future research. First, our sample was relatively small so we are unable to assess if our findings generalize to larger song databases.

They were posting from mastodon, I guess that’s just by default.