Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter

WWW, pp. 695-704, 2011.

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twitter idiomtwitter userinformation spreadhashtags spreadhashtag spreadMore(10+)
Weibo:
We study this issue on Twitter, analyzing the ways in which tokens known as hashtags spread on a network defined by the interactions among Twitter users

Abstract:

There is a widespread intuitive sense that different kinds of information spread differently on-line, but it has been difficult to evaluate this question quantitatively since it requires a setting where many different kinds of information spread in a shared environment. Here we study this issue on Twitter, analyzing the ways in which toke...More

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Introduction
  • Copyright is held by the International World Wide Web Conference Committee (IW3C2)
  • Distribution of these papers is limited to classroom use, and personal use by others.
  • Despite the fascination with these questions, the authors do not have a good quantitative picture of how this variation operates at a large scale
Highlights
  • A growing line of recent research has studied the spread of information on-line, investigating the tendency for people to engage in activities such as forwarding messages, linking to articles, joining

    Copyright is held by the International World Wide Web Conference Committee (IW3C2)
  • Groups, purchasing products, or becoming fans of pages after some number of their friends have done so [1, 4, 7, 9, 15, 20, 22, 23, 29]. The work in this area has far focused primarily on identifying properties that generalize across different domains and different types of information, leading to principles that characterize the process of on-line information diffusion and drawing connections with sociological work on the diffusion of innovations [27, 28]
  • As we begin to understand what is common across different forms of on-line information diffusion, it becomes increasingly important to ask about the sources of variation as well
  • In this paper we analyze sources of variation in how the most widely-used hashtags on Twitter spread within its user population. We find that these sources of variation involve not just differences in the probability with which something spreads from one person to another — the quantitative analogue of stickiness — and differences in a quantity that can be viewed as a kind of “persistence,” the relative extent to which repeated exposures to a piece of information continue to have significant marginal effects on its adoption
  • By studying the ways in which an individual’s use of widelyadopted Twitter hashtags depends on the usage patterns of their network neighbors, we have found that hashtags of different types and topics exhibit different mechanics of spread
  • Recent work has investigated this interplay of influence and homophily in the spreading of on-line behaviors [2, 8, 3, 19]; It would be interesting to look at how this varies across topics and categories of information as well — it is plausible, for example, that the joint mention of a political hashtag provides stronger evidence of user-to-user similarity than the analogous joint mention of hashtags on other topics, or that certain conversational idioms are significantly better indicators of similarity than others
Results
  • The authors simulate how a cascade that spreads according to the p(k) curve for some hashtag evolves when seeded with an initially active user sets of other hashtags.
  • Since the authors want to study how a hashtag blossoms from being used by a few starting nodes to a large number of users, the authors must be careful about how the authors select the size of the starting sets.
  • The authors believe that these initial set sizes capture the varying topology observed in Section 4 and are not too large as to guarantee wide-spreading cascade.
  • For 100 and 500 starting nodes the authors run five simulations on each (p(k), start set) pair, and for 1,000 starting nodes the authors run only two simulations
Conclusion
  • By studying the ways in which an individual’s use of widelyadopted Twitter hashtags depends on the usage patterns of their network neighbors, the authors have found that hashtags of different types and topics exhibit different mechanics of spread.
  • The adoption of politically controversial hashtags is especially affected by multiple repeated exposures, while such repeated exposures have a much less important marginal effect on the adoption of conversational idioms
  • This extension of information diffusion analysis, taking into account sources of variation across topics, opens up a variety of further directions for investigation.
  • The authors' analysis here suggests that a rich spectrum of differences may exist across topics as well
Summary
  • Introduction:

    Copyright is held by the International World Wide Web Conference Committee (IW3C2)
  • Distribution of these papers is limited to classroom use, and personal use by others.
  • Despite the fascination with these questions, the authors do not have a good quantitative picture of how this variation operates at a large scale
  • Results:

    The authors simulate how a cascade that spreads according to the p(k) curve for some hashtag evolves when seeded with an initially active user sets of other hashtags.
  • Since the authors want to study how a hashtag blossoms from being used by a few starting nodes to a large number of users, the authors must be careful about how the authors select the size of the starting sets.
  • The authors believe that these initial set sizes capture the varying topology observed in Section 4 and are not too large as to guarantee wide-spreading cascade.
  • For 100 and 500 starting nodes the authors run five simulations on each (p(k), start set) pair, and for 1,000 starting nodes the authors run only two simulations
  • Conclusion:

    By studying the ways in which an individual’s use of widelyadopted Twitter hashtags depends on the usage patterns of their network neighbors, the authors have found that hashtags of different types and topics exhibit different mechanics of spread.
  • The adoption of politically controversial hashtags is especially affected by multiple repeated exposures, while such repeated exposures have a much less important marginal effect on the adoption of conversational idioms
  • This extension of information diffusion analysis, taking into account sources of variation across topics, opens up a variety of further directions for investigation.
  • The authors' analysis here suggests that a rich spectrum of differences may exist across topics as well
Tables
  • Table1: Definitions of categories used for annotation
  • Table2: A small set of examples of members in each category
  • Table3: Median values for number of mentions, number of users, and number of mentions per user for different types of hashtags
  • Table4: Comparison of graphs induced by the first 500 early adopters of political hashtags and average hashtags. Column definitions: I. Average degree, II. Average triangle count, III. Average entering degree of the nodes in the border of the graphs, IV. Average number of nodes in the border of the graphs. The error bars indicate the 95% confidence interval of the average value of a randomly selected set of hashtags of the same size as Political
Download tables as Excel
Funding
  • This work has been supported in part by the MacArthur Foundation, a Google Research Grant, a Yahoo! Research Alliance Grant, and NSF grants IIS-0705774, IIS-0910664, IIS-0910453, and CCF-0910940
  • Brendan Meeder is supported by a NSF Graduate Research Fellowship
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