Post by account_disabled on Mar 11, 2024 3:07:09 GMT -5
The most recurring concepts in the messages of NO supporters were the opportunity to send Renzi home and the question of parliamentary immunity for regional councilors. The most cited hashtags and terms were: “#iovotono”, “votore” and “renzi”. Those in favor of the reform, however, focused attention on reducing the costs of politics and on the benefits deriving from the abolition of equal bicameralism. The most used hashtags and terms were “#bastunsi”, “vote” and “win”. The posts with the most interactions The analysis of the most viral contents on the internet highlights the relevance of videos. Those promoted by parties and politicians have been successful on Facebook.
In particular, the one from the 5 Star Movement Canada Phone Number showing the NO demonstration in Rome (over 2.4 million views) and the "Let's start again" commercial published by Matteo Renzi (over 2 million views). On YouTube, a favorite environment for younger people, "unofficial" ones of an informative and humorous nature emerge. An example of the first type is " The constitutional referendum summarized and explained simply " by YouTuber Alessandro Masala, which generated over 450,000 views. Among the latter we note “ Referendum 2016 Have you noticed that… ” by Daniele Doesn't Matter with over 150,000 views. Topology of the networks By analyzing the mentions on Twitter through the Social Network Analysis technique it is possible to visualize the conformation of the networks of accounts that have expressed themselves on the topic. In the following image
The largest names (nodes) are the most cited in the period analyzed (28/11-3/12), while the connecting lines (arcs) denote a citation (reply and retweet). The green color identifies quotes originating from YES accounts, while red indicates those originating from NO supporters. What emerges is a substantial polarization of positions, with NO networks clearly distant from those of YES. But the biggest issues, like Renzi, have multiple incoming quotes, often disparaging, also coming from his opponents. In conclusion, here are some things I have learned and which would be worth reflecting on together: – analyzing only Twitter is reductive and risks producing a distortion of the results. Better to extend data mining to the entire open web; – considering only the frequency of use of terms and hashtags produces an overestimation of the results. It is essential to focus attention on unique authors; – the most viral content was not “fake news” as some said, but the messages coming from the protagonists of the political debate – conversations on the web, if analyzed correctly, can be a reliable thermometer of political opinions
In particular, the one from the 5 Star Movement Canada Phone Number showing the NO demonstration in Rome (over 2.4 million views) and the "Let's start again" commercial published by Matteo Renzi (over 2 million views). On YouTube, a favorite environment for younger people, "unofficial" ones of an informative and humorous nature emerge. An example of the first type is " The constitutional referendum summarized and explained simply " by YouTuber Alessandro Masala, which generated over 450,000 views. Among the latter we note “ Referendum 2016 Have you noticed that… ” by Daniele Doesn't Matter with over 150,000 views. Topology of the networks By analyzing the mentions on Twitter through the Social Network Analysis technique it is possible to visualize the conformation of the networks of accounts that have expressed themselves on the topic. In the following image
The largest names (nodes) are the most cited in the period analyzed (28/11-3/12), while the connecting lines (arcs) denote a citation (reply and retweet). The green color identifies quotes originating from YES accounts, while red indicates those originating from NO supporters. What emerges is a substantial polarization of positions, with NO networks clearly distant from those of YES. But the biggest issues, like Renzi, have multiple incoming quotes, often disparaging, also coming from his opponents. In conclusion, here are some things I have learned and which would be worth reflecting on together: – analyzing only Twitter is reductive and risks producing a distortion of the results. Better to extend data mining to the entire open web; – considering only the frequency of use of terms and hashtags produces an overestimation of the results. It is essential to focus attention on unique authors; – the most viral content was not “fake news” as some said, but the messages coming from the protagonists of the political debate – conversations on the web, if analyzed correctly, can be a reliable thermometer of political opinions