Ranking Twitter Discussion Groups

Abstract:

A discussion group is a repeated, synchronized conversation organized around a specific topic. Groups are extremely valuable to the attendees, creating a sense of community among like-minded users. While groups may involve many users, there are many outside the group that would benefit from participation. However, finding the right group is not easy given their quantity and given topic overlap. We study the following problem: given a search query, find a good ranking of discussion groups. We describe a random walk model for how users select groups: starting with a group relevant to the query, a hypothetical user repeatedly selects an authoritative user in the group and then moves to a group according to what the authoritative user prefers. The stationary distribution of this walk yields a group ranking. We analyze this random walk model, demonstrating that it enjoys many natural properties of a desirable ranking algorithm. We study groups on Twitter where conversations can be organized via pre-designated hashtags. These groups are an emerging phenomenon and there are at least tens of thousands in existence today according to our calculations. Via an extensive collection of experiments on one year of tweets, we show that our model effectively ranks groups, outperforming several baseline solutions.

Source: Microsoft Research

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Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters

From the summary:

Conversations on Twitter create networks with identifiable contours as people reply to and mention one another in their tweets. These conversational structures differ, depending on the subject and the people driving the conversation. Six structures are regularly observed: divided, unified, fragmented, clustered, and inward and outward hub and spoke structures. These are created as individuals choose whom to reply to or mention in their Twitter messages and the structures tell a story about the nature of the conversation.

The Polarized Crowd network structure is only one of several different ways that crowds and conversations can take shape on Twitter. There are at least six distinctive structures of social media crowds which form depending on the subject being discussed, the information sources being cited, the social networks of the people talking about the subject, and the leaders of the conversation. Each has a different social structure and shape: divided, unified, fragmented, clustered, and inward and outward hub and spokes.

Source:  Pew Research Internet Project

Resources available:
Complete Report: Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters
Data Gallery: Examples of six kinds of Twitter social media networks
How Pew analyzed the data with nodexl
Fact Tank: How Pew mapped the conversation on Twitter

More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior

Abstract:

Is social media a valid indicator of political behavior? We answer this question using a random sample of 537,231,508 tweets from August 1 to November 1, 2010 and data from 406 competitive U.S. congressional elections provided by the Federal Election Commission. Our results show that the percentage of Republican-candidate name mentions correlates with the Republican vote margin in the subsequent election. This finding persists even when controlling for incumbency, district partisanship, media coverage of the race, time, and demographic variables such as the district’s racial and gender composition. With over 500 million active users in 2012, Twitter now represents a new frontier for the study of human behavior. This research provides a framework for incorporating this emerging medium into the computational social science toolkit.

Source: Social Science Resource Network

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Pew Research: Twitter Reaction to Events Often at Odds with Overall Public Opinion

Online Report:

The reaction on Twitter to major political events and policy decisions often differs a great deal from public opinion as measured by surveys. This is the conclusion of a year-long Pew Research Center study that compared the results of national polls to the tone of tweets in response to eight major news events, including the outcome of the presidential election, the first presidential debate and major speeches by Barack Obama.

At times the Twitter conversation is more liberal than survey responses, while at other times it is more conservative. Often it is the overall negativity that stands out. Much of the difference may have to do with both the narrow sliver of the public represented on Twitter as well as who among that slice chose to take part in any one conversation.

Source: Pew Research Center

Read online report: Twitter Reaction to Events Often at Odds with Overall Public Opinion