How a single user can generate a TT

On Saturday March 14, 2015 Anton Losada was choked on his breakfast when he saw the cover of the ABC newspaper and shared his discomfort on Twitter creating the hashtag #pídeleperdónalABC (apologizetoABC)


@antonlosada´s hashtag became the top of the Spanish trending topics (TT) and stayed there most of the day helped by multiple users outraged by the cover. The indignation turned into irony around the hashtag, creating an amazing festival of wit. The three factors of the spread (origin, impact of the message and context) were important.

The origin, @antonlosada, had an extensive network on Twitter, 103,958 followers at the time of posting the tweet and a high rate of propagation (71), measured with the index-H with traity.com. The message showed the cover of the news and did not need any more words to create an impact. The context formed by the people who were outraged by the message was very conducive to active participation. The three factors were aligned for the hashtag to succeed, as happened.

As always, we will see the temporal evolution of the spread measuring it in tweets per minute and comparing it with the reach (calculated as the sum of followers of the authors of the tweets at that time). These two values had different scales but the graph adjusts it to compare them. The reach peaks were tagged with the names of the users with more followers who have published at that time, to see whether or not they have affected the spread. As can be seen on the graph, the frequency increase after a peak reach , but not proportional always. We insist, the reach is not enough to spread.

We analyzed the most widespread tweets. The most popular users weren’t the most spread (except @la_tuerka). Instead, the authors had a smaller network but most shocking tweets published as @pblcarmona with 1,139 followers this this tweet was spread more than 1,000 times. There were several users with pseudonym as @BobEstropajo, @EsppeonzAguirre, @ForbesFlauta y @subversivos_ characterized by their sarcasm. The initiator of the hashtag, @antonlosada, placed two tweets on the top spread.

Another way to see the spread is by geographical origin of the tweets. In this case the location is obtained from the user profile (geolocation was very low, 0.64% of the tweets). We observed some geographical spread (see the Balearic islands) and a constant presence in Madrid.

Finally, we checked tweets frequency per minute to see what ranges become a TT. In this case, it was TT with a frequency between 35 and 100 tweets per minute, similar to other observations.

Conclusions

  1. Ideal conditions were given to propagate the message: a well-connected source, a compelling message and a very sensitive context
  2. As in other cases, users with a large reach (number of followers) spread the message but not in proportion to their network. The reach is not enough to propagate
  3. The most spread tweets didn’t came from users with largest reach. Instead, the authors had smaller networks but most shocking tweets. Again, the quality of the message
  4. Although the spread was national, a local spread is observed
  5. The frequency 35-100 tweets per minute became a TT in Spain
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