Twitter: Time, Individualism and Power
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Is the culture of a country associated with the way people use Twitter? The answer is a definite “Yes!” We analyzed more than 2.34 million geo-located user profiles in 30 countries plus their tweets for 10 weeks Blogpost: http://crowdresearch.org/blog/?p=6767 Paper:http://www.aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/view/6102
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Twitter: Time, Individualism and Power
- 1. Cultural Dimensions in Twitter: Time, Individualism and Power Ruth García-Gavilanes (UPF - Barcelona) Daniele Quercia (Yahoo! Barcelona) Alejandro Jaimes (Yahoo! Barcelona) 1
- 2. Culture 2
- 3. Microblogs 3
- 4. WHAT IS CULTURE ? 4
- 5. CULTURE Culture is a dimension that distinguishes members of one group or category of people from others 5
- 6. 6
- 7. 7
- 8. HOW TO MEASURE CULTURE • Geert Hofstede: Cultural dimensions Individualism Power Distance • Levine : Pace of Life (Geography of time) • Perception of time • Edward T. Hall Monochronic vs Polychronic 8
- 9. Pace of Life IndividualismPower Distance 9 Not considered Levine Hall
- 10. Culture and Social Media Can such differences also be captured from online interactions? 10
- 11. How to measure culture online? • Pace of Life Predictability (tweets, mentions) Tweets in working hours • Individualism vs. Collectivism Users interacting with others (mentions) • Power Distance : Popularity Follow, recommend and accept recommendation preferentially from more popular users (in-degree imbalance). 11
- 12. Sampling for 10 weeks in 2011 12 2.34 geolocated users follows #FollowFridday 12.6 K 1.9M 362 K 100>= out-degree <=1000
- 13. Top 30 countries to study The top 30 countries by # of users is representative of internet users 13 United States Brazil United Kingdom Indonesia Canada Mexico JapanNetherlands Venezuela SpainAustralia Germany FranceArgentina Chile South Korea Colombia IrelandSouth Africa India Philippines TurkeyItaly RussiaSweden New ZealandNorway MalaysiaBelgiumSingapore 4 5 6.5 7.0 7.5 8.0 8.5 Log(Internet Penetration) Log(UserCount) r = 0.63***
- 14. RESULTS 14
- 15. Pace of Life : Predictability Entropy a) tweets b) mentions − pi ( j)log2 pi ( j) j=1 Ni ∑pi ( j): 15 12AM 6AM 9AM 6 PM 9PM 12PM # tweets in working hours Interval j
- 16. Pace of Life : Predictability Tweets Mentio ns Users in working hours Pace of life **-0.62 **-0.68 **-0.58 16 The higher the pace of life , the more predictability The higher the pace of life the less fraction of users will tweet during working hours p < 0:005 (***), p < 0:05 (**), and p < 0.1 (*)
- 17. Individualism : Interacting with others Indonesia Venezuela Mexico JapanBrazilColombia Chile South Korea Argentina PhilippinesMalaysia Spain NetherlandsTurkey South Africa Singapore Ireland Canada FranceBelgiumSweden Norway New Zealand Italy Russia India Germany 80 85 90 95 100 20 40 60 80 Individualism Index FractionofEngagement 17 r= ***-0.55 p < 0:005 (***), p < 0:05 (**), and p < 0.1 (*) Collectivist countries interact more with others
- 18. Individualism : Interacting with others Indonesia Venezuela Mexico JapanBrazilColombia Chile South Korea Argentina PhilippinesMalaysia Spain NetherlandsTurkey South Africa Singapore Ireland Canada FranceBelgiumSweden Norway New Zealand Italy Russia India Germany 80 85 90 95 100 20 40 60 80 Individualism Index FractionofEngagement 18 Hong et al.. “Language matters in twitter: A large scale study” ICWSM 11
- 20. Power Distance: popularity imbalance 20 follows #FollowFridday Popularity imbalance for: seed followees followees recommended accepted recommendedseed
- 21. Power Distance: popularity imbalance Followers Followers/ Followees Users and followees **-0.62 **-0.67 Users and recommended user **-0.56 **-0.46 User and accepted recommended user -0.44 -0.29 21 p < 0:005 (***), p < 0:05 (**), and p < 0.1 (*) Users prefer to follow and recommend more popular users than themselves in countries with a higher power distance
- 22. Power Indonesia Venezuela Norway Malaysia Singapore Chile MexicoPhilippines Colombia United States South Korea IndiaBrazilCanada ArgentinaAustralia RussiaItalyNew Zealand SpainGermany Japan FranceSouth AfricaUnited KingdomIreland TurkeyNetherlands BelgiumSweden -1000 0 1000 2000 3000 4000 5000 30 60 90 Power Distance Index In-degreeImbalance 22
- 23. Power Indonesia Venezuela Norway Malaysia Singapore Chile MexicoPhilippines Colombia United States South Korea IndiaBrazilCanada ArgentinaAustralia RussiaItalyNew Zealand SpainGermany Japan FranceSouth AfricaUnited KingdomIreland TurkeyNetherlands BelgiumSweden -1000 0 1000 2000 3000 4000 5000 30 60 90 Power Distance Index In-degreeImbalance 23 27% of all blog trends are about artists and celebrities (Silang et al, 2011)
- 24. Why is this important? Indicator Pace of Time : Predictibility Individuali sm: Mentions Power Distance: ImbalanceMentions Users (%) GDP per capita ***0.55 **-0.57 **-0.41 **-0.48 Education ***0.58 **-0.51 -0.24 ***-0.60 Inequality ***-0.53 **0.49 *0.39 ***0.58 24
- 25. What is next? 25 • Language dependent features • More Cultural Dimensions • Temporal comparisons More features
- 26. What is next? 26 • User recommender • Individualistic vs. collectivistic ? • Predictable vs. unpredictable ? • Interfaces personalization • Do collectivist countries need additional features to interact easier? • More engagement? • Information Propagation • By knowing the cultural characteristics of users, can we increase re-tweet chance? Application
Notes de l'éditeur
- Good afternoon, thanks for coming to my talk. I am an intern at Yahoo Research and student of Universidad Pompeu de Fabra in Barcelona. Today I will talk about “Time, Individualism and Power” which is related to ..
- “Cultural Studies” and
- and Microblogs (as Janeth), but before entering into the details of this project, let’s give a closer look to the meaning of culture
- the concept of culture is used in many ways and it has different meanings, Some use it to refer to civilizations and others as the refinement of the mind relating it to education or art. However, the most widely accepted and used definition of culture in science comes from Geert Hofstedesstudies.
- In simple words, culture is a dimension that distinguishes members of one group or categories of people from others. For example, the comparison of behavior between individuals born and raised in the Unites States with individuals born and raised in Japan.
- Studying Culture involves studying and comparing the collective behavior learned in different societies as opposed to personality which focused on the study of individuals and their inherited and learned characteristics and as opposedto Human Nature which focused on the Universal inherited characteristics of humans.
- Culture influences the choices that we make, how we work, how we interact with others, etc. It is important therefore to take into accountthe cultural differences of groups for several reasons such as in business and marketing. So with this concenpt in mind, can we quantify culture?
- The answer is Yes we can! Over several years, a lot has been done to measure culture, specially from these threeanthropologists: Hofstede, Levine and Hall.
- Imagine the world divided by cultural dimensions where countries are categorized by the value of each dimension. Pace of LifeThe dimension of pace of life was studied by Levine. It measures how fast and punctual people perform tasks. For example, Switzerland is considered very punctual and fast as opposed to Brazil considered as more spontaneous and relaxed with time. Pace of life is also related to predictability of actions (which reflect the studies of Hall). More punctual and fast countries are considered more predictable than more spontaneous societies due to the tendency of having inflexible schedules and deadlines. ( I said in “general” because there are some exceptions such as Japan that tends to be punctual and fast in carrying out tasks but unpredictable in their actionsdue to their flexibility in changing their schedule (Hall). ) Power DistanceThe dimension of Power Distance measures the extend to which people feel confortable with unequal distribution of power. For example, China and Russia areconsidered countries with high power distance as opposed to USA and Australia.IndividualismThe dimension of Individualism measures the extend to which people are individualistic and maintain weak ties with others. For example, USA and Canada considered as highly individualistic countries as opposed to Indonesia or Brazil.
- Knowing that culture in the world has been measured through surveys and experimentsCan we detect differences in pace of life, individualism and power distance in the online world?
- We argue that yes, it is possible and to do so we have correlated different twitter metrics to each of the three dimensions (pace of time, individualism and power distance). For Pace of time, we measure whether you are predictable when you tweet and mention others and how many tweets you post as compared to other cultures. For Individualism we measure whether you interact more with others than people from other cultures and for Power Distance we measure whether you preferentially follow, recommend and accept recommendations from other users more popular than you, To understand what I mean with recommendation, lets give a look at the dataset
- In order to test our hypotheses, We started by collecting 12.6k geolocated seed users and their followee network during 10 weeks as well as all the tweetsposted from all of these users. From the tweets, we looked at those with the followfridayhashtag, Which is a tag used in 2011 to recommend users to follow. We collected the profile information of the users recommended and We checked at those that were accepted by the seed users. In total we arrive to 2,34 M users studied. By comparing the snapshots of the network of the seeds, we know which recommended user was accepted.
- In order to capture enough users, we selected countries with more than 5 K users which accounts for the top 30 countries. We tested the representativeness of the users of these 30 countriesWith the internet penetration per country and find that they are highly correlated. All this information lead us to haveThe time users post tweets adjusted by the corresponding time zoneWho users prefer to followWho recommends who andWho accepts whom through #FollowFridayhashtags
- First we have divided every working day in 5 time intervals and for pace of life, we have used the Entropy which measure the unpredictability of a user i posting in interval j (both for tweets and mentions)and the fraction of users posting during working hours (check later if it happens for all time intervals). For the entropy for tweets and mentions, we take the average entropy of all users for each country andcorrelate it with Pace of Life Index. For the # of tweets in working hours, we take the average per user in each country and correlated it with Pace of Life Index.
- We find that in all cases, the perason correlations (in this case negative) are significant and also high. It seems then that countries with a higher pace of life tend to be more predictable when they tweet and the higher the pace of life the less fraction of users will tweet during working hours
- We move to measure individualism now. Here we take the overall number of usersmentioning others and find a high correlation with their corresponding individualism index.
- It is interesting to notice the case of Germany. It has been found that German tweets received the least number ofmentions out of the 10 most common languages in Twitter (Hong, Convertino, and Chi 2011) and that in Germany,few comments are left in blogs, (Mandl 2009).---
- Here we present an example of the fraction of users mentioning others for two highly collectivist countries (Brazil and Indonesia) and three highly individualistic countries (USA, Canada and UK)For 5 time intervals in a day. We see that for all intervals, collectivist countries will tend to interact more with others than in individualistic ones.
- Now lets move to Power Distance. In our dataset, we have seed users, their followees and users recommended through FF hashtags. Some of whom are befriended by the seeds and some are not. Using these users, we measure power distance through popularity imbalance.We define popularity imbalance as the difference between your popularity and the popularity of who you follow, recommend and accept recommendations. Popularity here has been measured in two ways: by the in-degree value and the fraction between in-degree and out-degree.
- We find one more time a high correlation between countries with high power distance and the popularity imbalance ofwho they follow and recommend. Nevertheless, the correlation is not as high and significant for the acceptance ofrecommended users. It seems then that there is a higher popularity imbalance when users follow and recommend other users in countries with higher power distance(in case of questions) Nevertheless, the correlation is not as high for the recommendation acceptance.We explain this due to highly noisy nature of Follow Friday Recommendations.
- We also see here that for the majority of the cases, the countries follow the line of the correlation. Cite the study about Indonesian people and they following artists , pop singers, etc.
- We also see here that for the majority of the cases, the countries follow the line of the correlation. Cite the study about Indonesian people and they following artists , pop singers, etc.
- Why is this important?We go further, by correlating our findings with economic and social indicators which are: GDP per capita (GDP divided by the number of population), Education (the proportion of the GDP invested in Education) and Inequality (as the unequal distribution of family income ). We see that for almost all cases, the findings are are also correlated withGDP per capita, Education and Inequality. An exception is found between education and the interaction with other users.
- So what is next?So the most important question now to ask is when and how to apply this knowledge in building applications as to improve the user experience. We are currently studying the use of cultural features in recommender systems of social connections not onlyto increase the probability of acceptance but also to increase the tenure (longevity) of the recommendation in the network of the user. Generally applications now offer equal services without taking into consideration cultural differences. Interfaces could be personalized by considering for example the tendency of users to use twitter to chat and interact with others more than other countries. Cultural differences can also be studied in information propagation, for example does the likelihood of people interacting others increases the change of information propagation?