It isn't that simple. We want to know what percentage of tweets have property X given event Y. We can estimate that using a sample and error bars.

Note that the quotes specifically avoid this inference because the error bars would be silly. 500 million total tweets a day can't be talked about based on 7,000 hand categorized tweets.

But non staticians don't have any reference point for how powerful a predictor it is so a short hand is < 10% probably means in the error bars so less believable.

Taking your question non-rhetorically: because the error bars are wider than 10%[eg, 0, which finds a floor of 4% for anything based on polls], so anything less than that is consistent with p-hacking/random noise[eg see 1], and this is pervasive enough[2] that social science papers should be assumed garbage until proven otherwise.

0: http://slatestarcodex.com/2013/04/12/noisy-poll-results-and-...

1: https://www.explainxkcd.com/wiki/index.php/882:_Significant

2: https://en.wikipedia.org/wiki/Replication_crisis

It's not that no effects occur in this range, but if they do, they are not discernable from random fluctuations, noise and other statistical (non-)effects.

It can be real but unimportant. An extra drink every week might increase my liver cancer risk by 4%, but that's negligible if it's the difference between a 1 in 1 million chance and a 1 in 960,000 chance.