114,881 research outputs found
TwitterMancer: Predicting Interactions on Twitter Accurately
This paper investigates the interplay between different types of user
interactions on Twitter, with respect to predicting missing or unseen
interactions. For example, given a set of retweet interactions between Twitter
users, how accurately can we predict reply interactions? Is it more difficult
to predict retweet or quote interactions between a pair of accounts? Also, how
important is time locality, and which features of interaction patterns are most
important to enable accurate prediction of specific Twitter interactions? Our
empirical study of Twitter interactions contributes initial answers to these
questions.
We have crawled an extensive dataset of Greek-speaking Twitter accounts and
their follow, quote, retweet, reply interactions over a period of a month.
We find we can accurately predict many interactions of Twitter users.
Interestingly, the most predictive features vary with the user profiles, and
are not the same across all users.
For example, for a pair of users that interact with a large number of other
Twitter users, we find that certain "higher-dimensional" triads, i.e., triads
that involve multiple types of interactions, are very informative, whereas for
less active Twitter users, certain in-degrees and out-degrees play a major
role. Finally, we provide various other insights on Twitter user behavior.
Our code and data are available at https://github.com/twittermancer/.
Keywords: Graph mining, machine learning, social media, social network
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Museum Learning via Social Media: (How) Can Interactions on Twitter Enhance the Museum Learning Experience?
Museums are rich sources of artifacts, people and potential dialogic interactions. Recent developments in web technologies pose big challenges to museums to integrate such technologies in their learning provision. The study presented here is concerned with the potential of how school visits to museums can be enhanced by the use of social media. The Museum of London (MoL) is selected as the site of the study and the participants were a Year 9 History class (13-14 years old) in a secondary school in Milton Keynes. It draws on Falk and Dierking’s (2000) Contextual Model of Learning and considers evidence of meaning making from students’ tweets and activity on-site. Observational data during the visit, the visit’s Twitter stream and post-visit interview data with the participants is presented and analysed. It is argued that use of Twitter, a microblogging platform (http://twitter.com), enhances the social interaction around museum artifacts and thus, the process of shared construction of meaning making, which can enrich the museum experience
A customisable pipeline for continuously harvesting socially-minded Twitter users
On social media platforms and Twitter in particular, specific classes of
users such as influencers have been given satisfactory operational definitions
in terms of network and content metrics.
Others, for instance online activists, are not less important but their
characterisation still requires experimenting.
We make the hypothesis that such interesting users can be found within
temporally and spatially localised contexts, i.e., small but topical fragments
of the network containing interactions about social events or campaigns with a
significant footprint on Twitter.
To explore this hypothesis, we have designed a continuous user profile
discovery pipeline that produces an ever-growing dataset of user profiles by
harvesting and analysing contexts from the Twitter stream.
The profiles dataset includes key network and content-based users metrics,
enabling experimentation with user-defined score functions that characterise
specific classes of online users.
The paper describes the design and implementation of the pipeline and its
empirical evaluation on a case study consisting of healthcare-related campaigns
in the UK, showing how it supports the operational definitions of online
activism, by comparing three experimental ranking functions. The code is
publicly available.Comment: Procs. ICWE 2019, June 2019, Kore
Towards Measuring Adversarial Twitter Interactions against Candidates in the US Midterm Elections
Adversarial interactions against politicians on social media such as Twitter
have significant impact on society. In particular they disrupt substantive
political discussions online, and may discourage people from seeking public
office. In this study, we measure the adversarial interactions against
candidates for the US House of Representatives during the run-up to the 2018 US
general election. We gather a new dataset consisting of 1.7 million tweets
involving candidates, one of the largest corpora focusing on political
discourse. We then develop a new technique for detecting tweets with toxic
content that are directed at any specific candidate.Such technique allows us to
more accurately quantify adversarial interactions towards political candidates.
Further, we introduce an algorithm to induce candidate-specific adversarial
terms to capture more nuanced adversarial interactions that previous techniques
may not consider toxic. Finally, we use these techniques to outline the breadth
of adversarial interactions seen in the election, including offensive
name-calling, threats of violence, posting discrediting information, attacks on
identity, and adversarial message repetition
Validation of Dunbar's number in Twitter conversations
Modern society's increasing dependency on online tools for both work and
recreation opens up unique opportunities for the study of social interactions.
A large survey of online exchanges or conversations on Twitter, collected
across six months involving 1.7 million individuals is presented here. We test
the theoretical cognitive limit on the number of stable social relationships
known as Dunbar's number. We find that users can entertain a maximum of 100-200
stable relationships in support for Dunbar's prediction. The "economy of
attention" is limited in the online world by cognitive and biological
constraints as predicted by Dunbar's theory. Inspired by this empirical
evidence we propose a simple dynamical mechanism, based on finite priority
queuing and time resources, that reproduces the observed social behavior.Comment: 8 pages, 6 figure
Exploring interaction differences in Microblogging Word of Mouth between entrepreneurial and conventional service providers
In this study, we explore the interaction network properties of Microblogging Word of Mouth (MWOM), and how it is utilized by two different types of service providers, namely entrepreneurial and conventional. We use social network analysis, involving network metrics, sentiment, content and semantic analysis of real time data collected via Twitter, to compare two providers in terms of how they leverage MWOM in their social interactions. Results demonstrate that MWOM is utilized in an inherently different manner by an entrepreneurial provider, compared to a conventional one. Based on the findings, the study identifies distinctions between the entrepreneurial and conventional service providers in how they utilize MWOM on social media. Specifically, the entrepreneurial provider capitalizes on the interactive nature and dialogic capabilities of Twitter; whereas the conventional provider mostly relies on focal information sharing, thus neglecting the network members’ content creation and relationship building capability of social media networks. The study has significant implications as it provides key insights and lessons in terms of how companies should respond to emerging digital opportunities in their online social interactions
Information transfer in community structured multiplex networks
The study of complex networks that account for different types of
interactions has become a subject of interest in the last few years, specially
because its representational power in the description of users interactions in
diverse online social platforms (Facebook, Twitter, Instagram, etc.). The
mathematical description of these interacting networks has been coined under
the name of multilayer networks, where each layer accounts for a type of
interaction. It has been shown that diffusive processes on top of these
networks present a phenomenology that cannot be explained by the naive
superposition of single layer diffusive phenomena but require the whole
structure of interconnected layers. Nevertheless, the description of diffusive
phenomena on multilayer networks has obviated the fact that social networks
have strong mesoscopic structure represented by different communities of
individuals driven by common interests, or any other social aspect. In this
work, we study the transfer of information in multilayer networks with
community structure. The final goal is to understand and quantify, if the
existence of well-defined community structure at the level of individual
layers, together with the multilayer structure of the whole network, enhances
or deteriorates the diffusion of packets of information.Comment: 13 pages, 6 figure
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