A Network Based Stratification Approach for Summarizing Relevant Comment Tweets of News Articles

Chakraborty, Roshni; Bhavsar, Maitry; Dandapat, Sourav Kumar; Chandra, Joydeep

doi:10.1007/978-3-319-68783-4_3

Cited by 10 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…the number of news articles ranges from 10 − 86 and average number of sentences in the news article ranges from 17 − 45. Tweets Dataset For each of the events, we followed a pseudo relevance feedback based system [6,7] to extract the relevant tweets related to the event and we randomly select 500 relevant tweets for each event for our experimental analysis. Since we determine the stance of a tweet towards an event from the tweet text, we perform basic pre-processing on the tweet text, like removal of the hashtags, URL and user-ids from the tweet text.…”

Section: Dataset Collection and Pre-processing Detailsmentioning

confidence: 99%

Detecting Stance in Tweets : A Signed Network based Approach

Chakraborty¹,

Bhavsar²,

Dandapat³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Identifying user stance related to a political event has several applications, like determination of individual stance, shaping of public opinion, identifying popularity of government measures and many others. The huge volume of political discussions on social media platforms, like, Twitter, provide opportunities in developing automated mechanisms to identify individual stance and subsequently, scale to a large volume of users. However, issues like short text and huge variance in the vocabulary of the tweets make such exercise enormously difficult. Existing stance detection algorithms require either event specific training data or annotated twitter handles and therefore, are difficult to adapt to new events. In this paper, we propose a sign network based framework that use external information sources, like news articles to create a signed network of relevant entities with respect to a news event and subsequently use the same to detect stance of any tweet towards the event. Validation on 5, 000 tweets related to 10 events indicates that the proposed approach can ensure over 6.5% increase in average F1 score compared to the existing stance detection approaches.

show abstract

Section: Dataset Collection and Pre-processing Detailsmentioning

confidence: 99%

Detecting Stance in Tweets : A Signed Network based Approach

Chakraborty¹,

Bhavsar²,

Dandapat³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, we propose EnDSUM where we iteratively selecting the tweet that can ensure the maximum entropy of all the tweets and maximum diversity in summary. While selection of the tweet with maximum entropy ensures information coverage of a category, selection of the tweet with the maximum diversity ensures not multiple tweets from the same category are selected [5,6]. Therefore, at every iteration, we select the tweet ( * ), which has the maximum score by Equation 1.…”

Section: Proposed Approachmentioning

confidence: 99%

EnDSUM: Entropy and Diversity based Disaster Tweet Summarization

Garg¹,

Chakraborty²,

Dandapat³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

The huge amount of information shared in Twitter during disaster events are utilized by government agencies and humanitarian organizations to ensure quick crisis response and provide situational updates. However, the huge number of tweets posted makes manual identification of the relevant tweets impossible. To address the information overload, there is a need to automatically generate summary of all the tweets which can highlight the important aspects of the disaster. In this paper, we propose an entropy and diversity based summarizer, termed as EnDSUM, specifically for disaster tweet summarization. Our comprehensive analysis on 6 datasets indicates the effectiveness of EnDSUM and additionally, highlights the scope of improvement of EnDSUM.

show abstract

“…They find relevant tweets that share links to the news articles and use the text of the tweets as reference summaries for training their supervised learning model for news text summarization. The problem of generating relevant summarized social media discussion has also been tackled by Chakraborty et al (2017) wherein they use a network based unsupervised approach to handle the noise and diversity of tweets. Li et al (2016) describe EKNOT, their framework that summarizes events using both news and social media perspectives.…”

Section: Related Workmentioning

confidence: 99%

Unified Representation of Twitter and Online News Using Graph and Entities

et al. 2021

View full text Add to dashboard Cite

To improve consumer engagement and satisfaction, online news services employ strategies for personalizing and recommending articles to their users based on their interests. In addition to news agencies’ own digital platforms, they also leverage social media to reach out to a broad user base. These engagement efforts are often disconnected with each other, but present a compelling opportunity to incorporate engagement data from social media to inform their digital news platform and vice-versa, leading to a more personalized experience for users. While this idea seems intuitive, there are several challenges due to the disparate nature of the two sources. In this paper, we propose a model to build a generalized graph of news articles and tweets that can be used for different downstream tasks such as identifying sentiment, trending topics, and misinformation, as well as sharing relevant articles on social media in a timely fashion. We evaluate our framework on a downstream task of identifying related pairs of news articles and tweets with promising results. The content unification problem addressed by our model is not unique to the domain of news, and thus can be applicable to other problems linking different content platforms.

show abstract

A Network Based Stratification Approach for Summarizing Relevant Comment Tweets of News Articles

Cited by 10 publications

References 21 publications

Detecting Stance in Tweets : A Signed Network based Approach

Detecting Stance in Tweets : A Signed Network based Approach

EnDSUM: Entropy and Diversity based Disaster Tweet Summarization

Unified Representation of Twitter and Online News Using Graph and Entities

Contact Info

Product

Resources

About