I Link Search And Routing In Social Networks
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This paper proposes a general interaction model called iLink for social networks to accomplish tasks through peer collaboration. iLink models social networks as graphs with nodes having property vectors and probabilistic, dynamic topology. It develops a learning framework to understand how social networks create artifacts and build new applications by modeling node/link properties, developing topic models from interactions, and learning these models over time from message passing. The paper describes applying this model in a system called FAQtory for social search and message routing.
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The Semantic Evolution of Online Communities
This paper studies the semantic evolution of online communities over time. It constructs semantic graphs based on concepts and entities discussed in community forums between given time intervals. It analyzes the macro evolution of these graphs across communities by measuring graph properties like node count, diameter, entropy, and specializations over time. It finds that for concept graphs, these measures tend to converge over time, while for entity graphs, node count increases linearly but other measures converge. This provides insights into how semantics in online communities develop and differ between communities.
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Uk seminar
This document summarizes Pablo Aragón's research on modeling participation in online discussion platforms. The research aims to develop data-driven tools to analyze, model, and influence social behavior on online platforms to optimize their performance. Key findings include:
- Conversation threading increases reciprocity, online deliberation, and the complexity of discussion threads.
- A generative model incorporating reciprocity better reproduces the structure of discussion threads.
- Negative comments on proposals trigger more complex discussion cascades than neutral or positive comments.
- Future work includes exploring how conversation threading affects topic distributions and linguistic indications of reciprocity.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKING
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKING
The document presents a new model for intelligent social networks based on semantic tag ranking. It uses a multi-agent system approach with agents performing indexing and ranking. For indexing, it uses an enhanced Latent Dirichlet Allocation (E-LDA) model that optimizes LDA parameters. Tags above a threshold from E-LDA output are ranked using Tag Rank. Simulation results showed improvements in indexing and ranking over conventional methods. The model introduces semantics to social networks to improve search and link recommendation.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKING
Social Networks has become one of the most popular platforms to allow users to communicate, and share
their interests without being at the same geographical location. With the great and rapid growth of Social
Media sites such as Facebook, LinkedIn, Twitter...etc. causes huge amount of user-generated content.
Thus, the improvement in the information quality and integrity becomes a great challenge to all social
media sites, which allows users to get the desired content or be linked to the best link relation using
improved search / link technique. So introducing semantics to social networks will widen up the
representation of the social networks.
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Sensing Trending Topics in Twitter for Greater Jakarta Area
Information and communication technology grows so fast nowadays, especially related to the internet. Twitter is one of internet applications that produce a large amount of textual data called tweets. The tweets may represent real-world situation discussed in a community. Therefore, Twitter can be an important media for urban monitoring. The ability to monitor the situations may guide local government to respond quickly or make public policy. Topic detection is an important automatic tool to understand the tweets, for example, using non-negative matrix factorization. In this paper, we conducted a study to implement Twitter as a media for the urban monitoring in Jakarta and its surrounding areas called Greater Jakarta. Firstly, we analyze the accuracy of the detected topics in term of their interpretability level. Next, we visualize the trend of the topics to identify popular topics easily. Our simulations show that the topic detection methods can extract topics in a certain level of accuracy and draw the trends such that the topic monitoring can be conducted easily.
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present in the tweet. We consider the large clusters of the graph and approximate the stream
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first layer of a topic, and the iteration over the nodes provide a hierarchical decomposition.
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Hierarchical topics in texts generated by a stream
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Social and information networks have been extensively studied over years. This paper studies a new query on sub graph search on heterogeneous networks. Given an uncertain network of N objects, where each object is associated with a network to an underlying critical problem of discovering, top-k sub graphs of entities with rare and surprising associations returns k objects such that the expected matching sub graph queries efficiently involves, Compute all matching sub graphs which satisfy "Nebula computing requests" and this query is useful in ranking such results based on the rarity and the interestingness of the associations among nebula requests in the sub graphs. "In evaluating Top k-selection queries, "we compute information nebula using a global structural context similarity, and our similarity measure is independent of connection sub graphs". We need to compute the previous work on the matching problem can be harnessed for expected best for a naive ranking after matching for large graphs. Top k-selection sets and search for the optimal selection set with the large graphs; sub graphs may have enormous number of matches. In this paper, we identify several important properties of top-k selection queries, We propose novel top–K mechanisms to exploit these indexes for answering interesting sub graph queries efficiently.
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LAK'16 Conference paper presentation:
abstract:
In this paper, we present the results of an exploratory study that examined the problem of automating content analysis of student online discussion transcripts. We looked at the problem of coding discussion transcripts for the levels of cognitive presence, one of the three main constructs in the Community of Inquiry (CoI) model of distance education. Using Coh-Metrix and LIWC features, together with a set of custom features developed to capture discussion context, we developed a random forest classification system that achieved 70.3% classification accuracy and 0.63 Cohen’s kappa, which is significantly higher than values reported in the previous studies. Besides improvement in classification accuracy, the developed system is also less sensitive to overfitting as it uses only 205 classification features, which is around 100 times less features
than in similar systems based on bag-of-words features. We also provide an overview of the classification features most indicative of the different phases of cognitive presence that gives an additional insights into the nature of cognitive presence learning cycle. Overall, our results show great potential of the proposed approach, with an added benefit of providing further characterization of the cognitive presence coding scheme.
Using content and interactions for discovering communities in
1. The document proposes methods for discovering communities in social networks using content and interactions by modeling communities based on discussed topics and social connections between users. This allows discovering both user interests and popular topics within each community.
2. Bayesian models are used to extract latent communities from the network, assuming community relationships depend on user interests in topics and their links. Different models are proposed to handle different network structures like broadcast vs conversation networks.
3. The models aim to utilize both content and link information to discover communities in incomplete social networks with missing link information. A distance metric is learned using observed links and used for hierarchical clustering.
Slides: Concurrent Inference of Topic Models and Distributed Vector Represent...
Abstract: Topic modeling techniques have been widely used to uncover dominant themes hidden inside an unstructured document collection. Though these techniques first originated in the probabilistic analysis of word distributions, many deep learning approaches have been adopted recently. In this paper, we propose a novel neural network based architecture that produces distributed representation of topics to capture topical themes in a dataset. Unlike many state-of-the-art techniques for generating distributed representation of words and documents that directly use neighboring words for training, we leverage the outcome of a sophisticated deep neural network to estimate the topic labels of each document. The networks, for topic modeling and generation of distributed representations, are trained concurrently in a cascaded style with better runtime without sacrificing the quality of the topics. Empirical studies reported in the paper show that the distributed representations of topics represent intuitive themes using smaller dimensions than conventional topic modeling approaches.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
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Rostislav Yavorsky - Research Challenges of Dynamic Socio-Semantic Networks
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