As stated, this process is manual and probably tedious. Fortunately, it is not technically difficult to have it automated. Someone can build one single Web site where the reader can search for the news reports of the same incident on Web sites of different languages. Then the reader can check the most like'ed comments posted by people speaking those languages. This Web site should automatically translate, by way of Google Translate or any online translation engine, the entered keywords and submit them to representative Web sites, such as cnn.com, spiegel.de, elmundo.es, etc. In addition, the Web site should also gather such information from Facebook, where major news medias frequently provide news feeds and readers post comments that by default are already sorted by number of like's.
There are shortcomings in this opinion gathering method and the automation Web site. Although sampling bias is not unique to any specific polling method, it may be particularly evident in this passive sampling. But more importantly, while machine translation as of 2017 can do a good job with well written articles, it struggles with casual writing with spelling or grammatical errors, which a human can easily tolerate. Readers' comments on social networks have so many such errors that a human translator may have to step in to decipher what the passages exactly mean. If necessary, a group of volunteers from different countries have to work on such a Web site. Lastly, the people speaking a specific language are not necessarily of a specific nationality. But that's a minor point.