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Modelling of trends in Twitter using retweet graph dynamics  [PDF]
Marijn ten Thij,Tanneke Ouboter,Daniel Worm,Nelly Litvak,Hans van den Berg,Sandjai Bhulai
Computer Science , 2015, DOI: 10.1007/978-3-319-13123-8_11
Abstract: In this paper we model user behaviour in Twitter to capture the emergence of trending topics. For this purpose, we first extensively analyse tweet datasets of several different events. In particular, for these datasets, we construct and investigate the retweet graphs. We find that the retweet graph for a trending topic has a relatively dense largest connected component (LCC). Next, based on the insights obtained from the analyses of the datasets, we design a mathematical model that describes the evolution of a retweet graph by three main parameters. We then quantify, analytically and by simulation, the influence of the model parameters on the basic characteristics of the retweet graph, such as the density of edges and the size and density of the LCC. Finally, we put the model in practice, estimate its parameters and compare the resulting behavior of the model to our datasets.
Aggregate Characterization of User Behavior in Twitter and Analysis of the Retweet Graph  [PDF]
David R. Bild,Yue Liu,Robert P. Dick,Z. Morley Mao,Dan S. Wallach
Computer Science , 2014, DOI: 10.1145/2700060
Abstract: Most previous analysis of Twitter user behavior is focused on individual information cascades and the social followers graph. We instead study aggregate user behavior and the retweet graph with a focus on quantitative descriptions. We find that the lifetime tweet distribution is a type-II discrete Weibull stemming from a power law hazard function, the tweet rate distribution, although asymptotically power law, exhibits a lognormal cutoff over finite sample intervals, and the inter-tweet interval distribution is power law with exponential cutoff. The retweet graph is small-world and scale-free, like the social graph, but is less disassortative and has much stronger clustering. These differences are consistent with it better capturing the real-world social relationships of and trust between users. Beyond just understanding and modeling human communication patterns and social networks, applications for alternative, decentralized microblogging systems-both predicting real-word performance and detecting spam-are discussed.
Recommending Targeted Strangers from Whom to Solicit Information on Social Media  [PDF]
Jalal Mahmud,Michelle X. Zhou,Nimrod Megiddo,Jeffrey Nichols,Clemens Drews
Computer Science , 2014,
Abstract: We present an intelligent, crowd-powered information collection system that automatically identifies and asks target-ed strangers on Twitter for desired information (e.g., cur-rent wait time at a nightclub). Our work includes three parts. First, we identify a set of features that characterize ones willingness and readiness to respond based on their exhibited social behavior, including the content of their tweets and social interaction patterns. Second, we use the identified features to build a statistical model that predicts ones likelihood to respond to information solicitations. Third, we develop a recommendation algorithm that selects a set of targeted strangers using the probabilities computed by our statistical model with the goal to maximize the over-all response rate. Our experiments, including several in the real world, demonstrate the effectiveness of our work.
Good Friends, Bad News - Affect and Virality in Twitter  [PDF]
Lars Kai Hansen,Adam Arvidsson,Finn ?rup Nielsen,Elanor Colleoni,Michael Etter
Computer Science , 2011,
Abstract: The link between affect, defined as the capacity for sentimental arousal on the part of a message, and virality, defined as the probability that it be sent along, is of significant theoretical and practical importance, e.g. for viral marketing. A quantitative study of emailing of articles from the NY Times finds a strong link between positive affect and virality, and, based on psychological theories it is concluded that this relation is universally valid. The conclusion appears to be in contrast with classic theory of diffusion in news media emphasizing negative affect as promoting propagation. In this paper we explore the apparent paradox in a quantitative analysis of information diffusion on Twitter. Twitter is interesting in this context as it has been shown to present both the characteristics social and news media. The basic measure of virality in Twitter is the probability of retweet. Twitter is different from email in that retweeting does not depend on pre-existing social relations, but often occur among strangers, thus in this respect Twitter may be more similar to traditional news media. We therefore hypothesize that negative news content is more likely to be retweeted, while for non-news tweets positive sentiments support virality. To test the hypothesis we analyze three corpora: A complete sample of tweets about the COP15 climate summit, a random sample of tweets, and a general text corpus including news. The latter allows us to train a classifier that can distinguish tweets that carry news and non-news information. We present evidence that negative sentiment enhances virality in the news segment, but not in the non-news segment. We conclude that the relation between affect and virality is more complex than expected based on the findings of Berger and Milkman (2010), in short 'if you want to be cited: Sweet talk your friends or serve bad news to the public'.
Engaging Stakeholders through Twitter: How Nonprofit Organizations are Getting More Out of 140 Characters or Less  [PDF]
Kristen Lovejoy,Richard Waters,Gregory D. Saxton
Computer Science , 2011, DOI: 10.1016/j.pubrev.2012.01.005
Abstract: 140 characters seems like too small a space for any meaningful information to be exchanged, but Twitter users have found creative ways to get the most out of each Tweet by using different communication tools. This paper looks into how 73 nonprofit organizations use Twitter to engage stakeholders not only through their tweets, but also through other various communication methods. Specifically, it looks into the organizations' utilization of tweet frequency, following behavior, hyperlinks, hashtags, public messages, retweets, and multimedia files. After analyzing 4,655 tweets, the study found that the nation's largest nonprofits are not using Twitter to maximize stakeholder involvement. Instead, they continue to use social media as a one-way communication channel, as less than 20% of their total tweets demonstrate conversations and roughly 16% demonstrate indirect connections to specific users.
Bonobos Share with Strangers  [PDF]
Jingzhi Tan, Brian Hare
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0051922
Abstract: Humans are thought to possess a unique proclivity to share with others – including strangers. This puzzling phenomenon has led many to suggest that sharing with strangers originates from human-unique language, social norms, warfare and/or cooperative breeding. However, bonobos, our closest living relative, are highly tolerant and, in the wild, are capable of having affiliative interactions with strangers. In four experiments, we therefore examined whether bonobos will voluntarily donate food to strangers. We show that bonobos will forego their own food for the benefit of interacting with a stranger. Their prosociality is in part driven by unselfish motivation, because bonobos will even help strangers acquire out-of-reach food when no desirable social interaction is possible. However, this prosociality has its limitations because bonobos will not donate food in their possession when a social interaction is not possible. These results indicate that other-regarding preferences toward strangers are not uniquely human. Moreover, language, social norms, warfare and cooperative breeding are unnecessary for the evolution of xenophilic sharing. Instead, we propose that prosociality toward strangers initially evolves due to selection for social tolerance, allowing the expansion of individual social networks. Human social norms and language may subsequently extend this ape-like social preference to the most costly contexts.
Safe Deals Between Strangers  [PDF]
H. M. Gladney
Computer Science , 1999,
Abstract: E-business, information serving, and ubiquitous computing will create heavy request traffic from strangers or even incognitos. Such requests must be managed automatically. Two ways of doing this are well known: giving every incognito consumer the same treatment, and rendering service in return for money. However, different behavior will be often wanted, e.g., for a university library with different access policies for undergraduates, graduate students, faculty, alumni, citizens of the same state, and everyone else. For a data or process server contacted by client machines on behalf of users not previously known, we show how to provide reliable automatic access administration conforming to service agreements. Implementations scale well from very small collections of consumers and producers to immense client/server networks. Servers can deliver information, effect state changes, and control external equipment. Consumer privacy is easily addressed by the same protocol. We support consumer privacy, but allow servers to deny their resources to incognitos. A protocol variant even protects against statistical attacks by consortia of service organizations. One e-commerce application would put the consumer's tokens on a smart card whose readers are in vending kiosks. In e-business we can simplify supply chain administration. Our method can also be used in sensitive networks without introducing new security loopholes.
Efficiency of Human Activity on Information Spreading on Twitter  [PDF]
A. J Morales,J. Borondo,J. C. Losada,R. M. Benito
Computer Science , 2014, DOI: 10.1016/j.socnet.2014.03.007
Abstract: Understanding the collective reaction to individual actions is key to effectively spread information in social media. In this work we define efficiency on Twitter, as the ratio between the emergent spreading process and the activity employed by the user. We characterize this property by means of a quantitative analysis of the structural and dynamical patterns emergent from human interactions, and show it to be universal across several Twitter conversations. We found that some influential users efficiently cause remarkable collective reactions by each message sent, while the majority of users must employ extremely larger efforts to reach similar effects. Next we propose a model that reproduces the retweet cascades occurring on Twitter to explain the emergent distribution of the user efficiency. The model shows that the dynamical patterns of the conversations are strongly conditioned by the topology of the underlying network. We conclude that the appearance of a small fraction of extremely efficient users results from the heterogeneity of the followers network and independently of the individual user behavior.
Optimizing The Selection of Strangers To Answer Questions in Social Media  [PDF]
Jalal Mahmud,Michelle Zhou,Nimrod Megiddo,Jeffrey Nichols,Clemens Drews
Computer Science , 2014,
Abstract: Millions of people express themselves on public social media, such as Twitter. Through their posts, these people may reveal themselves as potentially valuable sources of information. For example, real-time information about an event might be collected through asking questions of people who tweet about being at the event location. In this paper, we explore how to model and select users to target with questions so as to improve answering performance while managing the load on people who must be asked. We first present a feature-based model that leverages users exhibited social behavior, including the content of their tweets and social interactions, to characterize their willingness and readiness to respond to questions on Twitter. We then use the model to predict the likelihood for people to answer questions. To support real-world information collection applications, we present an optimization-based approach that selects a proper set of strangers to answer questions while achieving a set of application-dependent objectives, such as achieving a desired number of answers and minimizing the number of questions to be sent. Our cross-validation experiments using multiple real-world data sets demonstrate the effectiveness of our work.
t factor: A metric for measuring impact on Twitter  [PDF]
Lutz Bornmann,Robin Haunschild
Computer Science , 2015,
Abstract: Based on the definition of the well-known h index we propose a t factor for measuring the impact of publications (and other entities) on Twitter. The new index combines tweet and retweet data in a balanced way whereby retweets are seen as data reflecting the impact of initial tweets. The t factor is defined as follows: A unit (single publication, journal, researcher, research group etc.) has factor t if t of its Nt tweets have at least t retweets each and the other (Nt-t) tweets have <=t retweets each.
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