The social media genome: Modeling individual topic-specific behavior in social media

@article{Bogdanov2013TheSM,
  title={The social media genome: Modeling individual topic-specific behavior in social media},
  author={Petko Bogdanov and Michael Busch and Jeff Moehlis and Ambuj K. Singh and Boleslaw K. Szymanski},
  journal={2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)},
  year={2013},
  pages={236-242}
}
  • Petko BogdanovMichael Busch B. Szymanski
  • Published 1 July 2013
  • Computer Science, Biology
  • 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)
Information propagation in social media depends not only on the static follower structure but also on the topic-specific user behavior. Hence novel models incorporating dynamic user behavior are needed. To this end, we propose a model for individual social media users, termed a genotype. The genotype is a per-topic summary of a user's interest, activity and susceptibility to adopt new information. We demonstrate that user genotypes remain invariant within a topic by adopting them for… 

Figures and Tables from this paper

Modeling individual topic-specific behavior and influence backbone networks in social media

It is demonstrated that user genotypes remain invariant within a topic by adopting them for classification of new information spread in large-scale real networks, and that knowledge of user genotype and influence backbones allows for the design of effective strategies for latency minimization of topic-specific information spread.

Discovering and tracking query oriented active online social groups in dynamic information network

This paper investigates the problem of discovering and tracking time-sensitive activity driven user groups in dynamic social networks for a given input query consisting a set of topics and proposes an index-based method to incrementally track the evolution of groups with a lower computational cost.

Predicting the Spread of a New Tweet in Twitter

This paper proposes topic based approach to predict the spread of a new tweet from a particular user in online social network namely in Twitter based on latent content interests of users and the structural characteristics of the underlying social network.

Topical Alignment in Online Social Systems

This work proposes an approach based on the use of hashtags to extract information topics from Twitter messages and model users' interests, and shows that topical alignment provides interesting information that can eventually allow inferring users' connectivity.

Discovering and Tracking Active Online Social Groups

This paper develops two baseline solutions to discover effective social groups and proposes an index-based method to incrementally track the evolution of groups with a lower computational cost and some interesting observations on the temporal evolution of the discovered social groups.

Persuasion driven influence propagation in social networks

  • Terrence LeungK. Chung
  • Business
    2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)
  • 2014
A Persuasiveness Aware Cascade model which considers social persuasion in influence propagation is proposed, and experiments suggest that the proposed model with the new social persuasion measures is more effective in describing real-world influence propagation than the well-studied propagation models for influence maximization.

Proximity, interactions, and communities in social networks : properties and applications

This thesis proposes techniques for analyzing human relationships in terms of geographical proximity, face-to-face interactions, and communities, and uses URLs that people share with their followers on social media to personalize the ranking of information to analyze how social media tunnels the flow of information in the network.

Characterizing Topics in Social Media Using Dynamics of Conversation

A set of unique features are introduced that capture patterns of discourse, allowing us to empirically explore the relationship between a topic and the conversations it induces, and set a paradigm for analyzing information dissemination through the user reactions that arise from a topic, eliminating the need to analyze the involved text of the discussions.

Topic dynamics in Weibo: a comprehensive study

This work comprehensively disclose the topic dynamics in Weibo from the perspective of time, geography, demographics, emotion, retweeting and correlation to provide insights for topic-related applications in online social media, such as user profiling, event detection, trend tracking or content recommendation.

Beyond the Power of Mere Repetition: Forms of Social Communication on Twitter through the Lens of Information Flows and Its Effect on Topic Evolution

This paper defines the communication modality constructs, and classify topics based on three dimensions: user involvement, information flow depth, and topic inter-relations, which substantially extend the traditional focus in user interaction analysis.

References

SHOWING 1-10 OF 28 REFERENCES

Inferring the Diffusion and Evolution of Topics in Social Communities

A novel and principled probabilistic model is proposed which casts the task as an joint inference problem, taking into consideration of textual documents, social influences, and topic evolution in a unified way, and performs significantly better than existing methods.

What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities

An efficient hybrid approach based on a linear regression for predicting the spread of an idea in a given time frame is presented and it is shown that a combination of content features with temporal and topological features minimizes prediction error.

Maximizing the spread of influence through a social network

An analysis framework based on submodular functions shows that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models, and suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.

What is Twitter, a social network or a news media?

This work is the first quantitative study on the entire Twittersphere and information diffusion on it and finds a non-power-law follower distribution, a short effective diameter, and low reciprocity, which all mark a deviation from known characteristics of human social networks.

Structure and dynamics of information pathways in online media

An on-line algorithm that relies on stochastic convex optimization to efficiently solve the dynamic network inference problem and studies the evolution of information pathways in the online media space.

Tie Formation on Twitter: Homophily and Structure of Egocentric Networks

This work proposes a variety of attributes along which homophily can be measured between individuals: including demographic attributes, activity-specific attributes and content-based attributes, and categorizes ego network structures as generators, mediators and receptors based on a measure called ego ratio.

Identifying topical authorities in microblogs

This work proposes a set of features for characterizing social media authors, including both nodal and topical metrics, and shows how probabilistic clustering over this feature space, followed by a within-cluster ranking procedure, can yield a final list of top authors for a given topic.

Characterizing Microblogs with Topic Models

A scalable implementation of a partially supervised learning model (Labeled LDA) that maps the content of the Twitter feed into dimensions that correspond roughly to substance, style, status, and social characteristics of posts is presented.

Extracting influential nodes on a social network for information diffusion

This paper addresses the combinatorial optimization problem of finding the most influential nodes on a large-scale social network for two widely-used fundamental stochastic diffusion models and proposes a method of efficiently finding a good approximate solution to the problem under the greedy algorithm on the basis of bond percolation and graph theory.

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

The first large-scale validation of the "complex contagion" principle from sociology, which posits that repeated exposures to an idea are particularly crucial when the idea is in some way controversial or contentious, is provided.