This document discusses analyzing cultural differences in social media use by summarizing two case studies:
1) Analyzing user engagement by language for tweets about the 2012 US elections found higher engagement in Farsi and Mandarin relative to English, indicating these elections were of great interest to Iranian and Chinese audiences.
2) Analyzing tweets about the Rock am Ring music festival by country found Germany, United States, and Brazil to be most engaged on Twitter for this event. The analysis demonstrates how cultural insights can be gained by examining social media data across languages and countries.
2. Why cultural analysis
– there are significant differences in the general
characteristics of the Twitter network in different
countries,
– And more generally, in the different social media,
– differences should be taken into account in
designing crawling and preservation strategies,
3.
4. US Elections 2012 Crawl
• We crawled tweets on the US elections 2012
using some target key words
(e.g., obama, romney, republican,…) from 1st
to 11th November 2012 to
• Let’s analyze them from a language
perspective
5. What about user engagement
• Do people engage in the same way whatever
the language?
• How can we measure user engagement ?
• There is many ways, one simple is:
– The average number of tweets by user in a specific
language.
7. Farsi and Mandarin, the us election’s
languages.
• User engagement is higher in Farsi or in Mandarin
than in English
– It means, there is fewer people tweeting in Farsi or
Mandarin, but, people when considering people
tweeting about the uselection, then people tweeting
in Farsi or in Mandarin tweets in average more about
uselection than people tweeting in english.
– It reveals an important cultural information taken
out from the social media. Us elections get a great
audience from farsi (Iran) speakers and mandarin
(Chinese).
8.
9. Rock am Ring campain
• Let’s do the same analysis on Rock am Ring
event, by again crawling tweets from a set of
key words related to the event
• But this time, let’s analyze countries
• The country information can be in majority of
cases extracted from the user profiles.