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In many countries the public's main source of information about science and technology is the mass media. Unfortunately, in recent years traditional journalism has experienced a collapse, and science journalism has been a major casualty. One potential remedy is to encourage scientists to write for news media about science. On these general news platforms, scientists' stories would have to compete for attention with other news stories on hard (e.g. politics) and entertaining (e.g. celebrity news) topics written by professional writers. Do they stand a chance? This study aimed to quantitatively characterize audience interactions as an indicator of interest in science news stories authored by early career scientists (henceforth 'scientists') trained to function as science reporters, as compared to news items written by reporters and published in the same news outlets. To measure users' behavior, we collected data on the number of clicks, likes, comments and average time spent on page. The sample was composed of 150 science items written by 50 scientists trained to contribute popular science stories in the Davidson Institute of Science Education reporters' program and published on two major Israeli news websites-Mako and Ynet between July 2015 to January 2018. Each science item was paired with another item written by the website's organic reporter, and published on the same channel as the science story (e.g., tourism, health) and the same close time. Overall significant differences were not found in the public's engagement with the different items. Although, on one website there was a significant difference on two out of four engagement types, the second website did not have any difference, e.g., people did not click, like or comment more on items written by organic reporters than on the stories written by scientists. This creates an optimistic starting point for filling the science news void by scientists as science reporters.
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Stored only when both papers are in the corpus — 47 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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