Social context summarization

Yang, Zi; Cai, Kefeng; Tang, Jie; Zhang, Li; Su, Zhong; Li, Juanzi

doi:10.1145/2009916.2009954

Cited by 78 publications

(50 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, often a single sentence is not sufficient to catch its meaning and even human beings need a context. In contrast to [13], we believe that a context does not provide redundant information, but allows to precise and extend sentence meaning. Therefore, we introduce an algorithm to smooth a candidate sentence by its local context, i.e.…”

Section: Introductionmentioning

confidence: 94%

A Method for Short Message Contextualization: Experiments at CLEF/INEX

Ermakova

2015

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. This paper presents the approach we developed for automatic multi-document summarization applied to short message contextualization, in particular to tweet contextualization. The proposed method is based on named entity recognition, part-of-speech weighting and sentence quality measuring. In contrast to previous research, we introduced an algorithm from smoothing from the local context. Our approach exploits topic-comment structure of a text. Moreover, we developed a graph-based algorithm for sentence reordering. The method has been evaluated at INEX/CLEF tweet contextualization track. We provide the evaluation results over the 4 years of the track. The method was also adapted to snippet retrieval and query expansion. The evaluation results indicate good performance of the approach.

show abstract

Section: Introductionmentioning

confidence: 94%

A Method for Short Message Contextualization: Experiments at CLEF/INEX

Ermakova

2015

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…P α (y i |y (i) j ) will be modeled using Bernoulli distribution and governed by parameter α as shown in (4).…”

Section: Problem Formulationmentioning

confidence: 99%

Detecting Marionette Microblog Users for Improved Information Credibility

Wang

Fan

Gao

et al. 2015

J. Comput. Sci. Technol.

View full text Add to dashboard Cite

In this paper, we propose to detect a special group of microblog users: the "marionette" users, who are created or employed by backstage "puppeteers", either through programs or manually. Unlike normal users that access microblog for information sharing or social communication, the marionette users perform specific tasks to earn financial profits. For example, they follow certain users to increase their "statistical popularity", or retweet some tweets to amplify their "statistical impact". The fabricated follower or retweet counts not only mislead normal users to wrong information, but also seriously impair microblog-based applications, such as hot tweets selection and expert finding. In this paper, we study the important problem of detecting marionette users on microblog platforms. This problem is challenging because puppeteers are employing complicated strategies to generate marionette users that present similar behaviors as normal users. To tackle this challenge, we propose to take into account two types of discriminative information: 1) individual user tweeting behavior and 2) the social interactions among users. By integrating both information into a semi-supervised probabilistic model, we can effectively distinguish marionette users from normal ones. By applying the proposed model to one of the most popular microblog platforms (Sina Weibo) in China, we find that the model can detect marionette users with F -measure close to 0.9. In addition, we apply the proposed model to calculate the marionette ratio of the top 200 most followed microbloggers and the top 50 most retweeted posts in Sina Weibo. To accelerate the detecting speed and reduce feature generation cost, we further propose a light-weight model which utilizes fewer features to identify marionettes from retweeters.

show abstract

“…We also compare to DWFG [24] which is based on conditional random fields and simultaneously treats messages and web documents as 'wings' in a dual wing factor graph, where factors are assigned to individual tweet and sentence features in the graph. A standard sum-product algorithm which is an approximate and relatively timeconsuming inference approach is used to determine the key sentences and the important messages.…”

Section: Baselinesmentioning

confidence: 99%

Selecting Social Media Responses to News: A Convex Framework Based On Data Reconstruction

Chen

et al. 2015

Proceedings of the 2015 SIAM International Conference on Data Mining

View full text Add to dashboard Cite

With the explosive growth of social media, it has gained significantly increasing attention from both journalists and their readership in recent years by enhancing the reading experience with its timeliness, high participation, interactivity, etc. On the other hand, the popularity of social media services such as Twitter also leads to the challenge of information overload by generating thousands of responses (tweets) for each article of hot news, which will be overwhelming for readers. In this paper, we address the problem of selecting a representative subset of responses to news in order to deliver the most important information. We consider different criteria regarding the importance of the selected subset, and treat the problem from the data reconstruction perspective with concerns for both quality and generalizability of the selection. The intuition behind our work is that a good selection should be relevant from two levels: i) at the message level, it brings readers new information as much as possible or generalizes other people's opinions comprehensively; ii) at the text level, it is able to reconstruct the corpus. Specifically, the task of selecting responses to news can be formulated as a convex optimization problem where sparse non-negative weights are introduced for all the responses indicating whether they are selected or not. Several gradient based optimization and step size selection methods are also investigated in this paper to achieve a faster rate of convergence. More importantly, the proposed framework evaluates the utility of a set of responses jointly and therefore is able to reduce redundancy of the selected responses. We evaluate our approach on real-world data obtained from Twitter, and the results demonstrate superior performance over the state of the art in both accuracy and generalizability.

show abstract

Social context summarization

Cited by 78 publications

References 28 publications

A Method for Short Message Contextualization: Experiments at CLEF/INEX

A Method for Short Message Contextualization: Experiments at CLEF/INEX

Detecting Marionette Microblog Users for Improved Information Credibility

Selecting Social Media Responses to News: A Convex Framework Based On Data Reconstruction

Contact Info

Product

Resources

About