▍國際傳媒：2021/10/15~2021/10/21

????  JOURNALIST’S RESOURCE 哈佛大學肖倫斯坦中心《記者資源》*｜20211019

✅  5 tips for avoiding mistakes in news headlines about health and medical research

關於健康醫學研究 避免新聞標題犯錯的 5 個小秘訣

Bad headlines on stories about health and medical research can be especially harmful considering many people make decisions that affect their personal health and safety — and the health and safety of loved ones — based on news reports.

*編註：《記者資源》是哈佛大學甘迺迪學院肖倫斯坦媒體政治與公共政策研究中心（Shorenstein Center）底下的一個計劃網站。

????  AXIOS 美國新興數位新聞網站《AXIOS》｜20211019

✅  Facebook doubling down on curated News Tab

臉書加倍致力於發展編輯精選新聞標籤

The News Tab, a separate destination for news on Facebook from publishers selected by the tech giant, has helped the company address regulatory scrutiny that it doesn’t do enough to combat misinformation.

????  FORBES 美國商業雜誌《富比士》｜20211020

✅  How MEL Tells Fascinating Male-Focused Stories

《MEL》雜誌如何述說聚焦在男性的迷人故事？

MEL is one of the rare publications that focuses on current topics and stories through a male lens without diving into a purely political stance. MEL stopped publishing in March of 2021 and restarted in August following Recurrent’s acquisition of the brand. 

????  Nieman Lab 哈佛大學尼曼新聞基金會《尼曼新聞實驗室》｜20211020

✅  It’s time to create an alternative path into a journalism career

是時候建立通往新聞事業的替代道路了！

American journalists look less and less like the country they cover — in terms of race, class, and background. We need to expand the pool of people who can enter the industry, and an idea from K-12 education might help.

????  NPR 美國《全國公共廣播電台》｜20211021

✅  Memes about COVID-19 helped us cope with life in a pandemic, a new study finds 

迷因有益健康！新研究發現：COVID-19 迷因幫助我們在疫情大流行時應對生活

Does a meme a day keep the doctor away? Not quite, but it looks like it may help, according to one recent study.

????  PROTOCOL 美國科技媒體《協定》*｜20211021

✅  Twitter’s own research shows that it’s a megaphone for the right. But it’s complicated.

推特研究表明：自家平台是右翼的揚聲器！但這很複雜。

Twitter is publicly sharing research findings today that show that the platform’s algorithms amplify tweets from right-wing politicians and content from right-leaning news outlets more than people and content from the political left.

*編註：美國科技媒體《協定》專門報導科技人、科技力與科技政治。

整理：朱弘川╱編譯：鄭凱榕