Researchers, many of whom have inadequate statistical training, are academics facing publish-or-perish and weird publication barriers (p-value filtering, premium on sexy new results over well-researched incremental advances). The more senior authors who have tenure and are safe from some of the incentives still rely on lab funding, which is similarly competitive, and also tend to people whose methodological training is substantially out of date. Most journal reviewers are subject matter experts, some with limited statistical training.
Universities put out press releases to support authors without being equipped to evaluate the quality of the work or having an incentive to contextualize limitations of the work. Science journalists largely breathlessly report the original press release without reading (or in some cases having access to) the original article. They tend to have a good understanding of jargon but a poor understanding of design. There is no notion of including comment from critical authors, and there is certainly no notion of critiquing the design, replicating the experiment (either a limited replication using the author's code / data, or a more thorough replication to obtain the substantive result). Then the blog-spam people play broken telephone with the initial science reporting. Well-meaning and interested users post these articles to reddit, HN, social media, etc. credulously reporting the headline of the article they read. Most comments in the discussion read the title, skip the article (let alone the study) and proceed assuming it's true, and normally take the form "This validates some other belief I have, so it's true" or "Duh why did they even do this study?", both of which are not useful.
I see very little evidence this process stems from the article's implied undertone that blog spam is costing legitimate journalists money and starving them of resources they need to do a good job. Even very well-funded sites suffer from this. The problem is the incentives. Imagine a science website that posts only 1/10th the number of posts, but deep-dives all of the ones they do.
Let me proposal an alternate model: As a user or a journalist, if you aren't trained enough to read the study, don't post an article about it. Training does not mean subject matter training (as in you understand whatever the specific topic at hand is), although that is useful. Training means enough numeracy to be able to evaluate the work. This should cripple the flow of science communication but vastly increase its quality.
Posting an article you can't understand and relying on the "system" to ensure it's true is the same kind of broken process that leads to people circulating conspiracy theory stuff or stuff we all agree is junk science. This should be especially followed in disciplines like social psychology, applied economics, neuroscience, evolutionary anthropology or evolutionary psychology, medical research, nutrition research, and other fields where experimental or quasi-experimental design are more difficult.