Surveying public opinion using label prediction on social media data

Stanojević, Marija; Alshehri, Jumanah; Obradović, Zoran

doi:10.1145/3341161.3342861

Cited by 9 publications

(5 citation statements)

References 10 publications

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“…News outlets and social platforms are recommending most relevant user posts to keep the attention of busy readers [33]. Many studies focus on mining the user opinion from social media [2,3,32] and online news comments [1,15,35,37]. Other works look into bias in the news, and its influence on user-generated content [31,36].…”

Section: Related Workmentioning

confidence: 99%

Stay on Topic, Please: Aligning User Comments to the Content of a News Article

Alshehri

Stanojević

Dragut

et al. 2021

Lecture Notes in Computer Science

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Social scientists have shown that up to 50% of the comments posted to a news article have no relation to its journalistic content. In this study we propose a classification algorithm to categorize user comments posted to a news article based on their alignment to its content. The alignment seeks to match user comments to an article based on similarity of content, entities in discussion, and topics. We propose a BERTAC, BERT-based approach that learns jointly article-comment embeddings and infers the relevance class of comments. We introduce an ordinal classification loss that penalizes the difference between the predicted and true labels. We conduct a thorough study to show influence of the proposed loss on the learning process. The results on five representative news outlets show that our approach can learn the comment class with up to 36% average accuracy improvement comparing to the baselines, and up to 25% comparing to the BA-BC. BA-BC is our approach that consists of two models aimed to capture dis-jointly the formal language of news articles and the informal language of comments. We also conduct a user study to evaluate human labeling performance to understand the difficulty of the classification task. The user agreement on comment-article alignment is "moderate" per Krippendorff's alpha score, which suggests that the classification task is difficult. Keywords: Text mining • Text classification • Online news • News comments • Relevancy • Understanding user-generated text J. Alshehri and M. Stanojevic-contributed equally.

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Section: Related Workmentioning

confidence: 99%

Stay on Topic, Please: Aligning User Comments to the Content of a News Article

Alshehri

Stanojević

Dragut

et al. 2021

Lecture Notes in Computer Science

Self Cite

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“…In Manoj Sethi et al [2], Twitter is the data source used mainly to gather tweets specific to the corona virus. 3 datasets were used totally, Ttweets with hashtag #coronavirus -Dataset 1, Table 9 Data sources Papers Data Source Manoj Sethi [1], Gonzalo A. Ruz [4], Xiaohui Tao [6], Priyanka Arora [8],Abdul Hasib Uddin [10], Nafiz Al Asad [12], Fazeel Abid [14], Badr Ait Hammou [15], Jwan Syaruf [20], Guangyao Shen [25],Akshi Kumar [26],Shakeel Ahmed [30], Mohammed Jabreel [32], Fuhai Chen [37], Mehmet Umut Salur [38], Kashif Ayyab [43], Ali Feizollah [44], Wei Lun Lim [53], Le Hoang Son [55], Rywan Khan [58],Priti Sharma [60], Fang Yao [61],Yingwei Yan [62],Samah Maneoue [64], Veny Amilia Fitri [66],Marouane Biyali [68], Jonathan G. D. Haib [70], Shenghio Liu [71], Mondher Bouazizi [75], Govin Gaikwad [79],Marija Stanojevic [81],…”

Section: Data Sourcementioning

confidence: 99%

“…MokhlesurRahman [100], Shivam Behl [101] Twitter Xingwei Yang [5], Lichen Chang [11],Kanthinee Katchapakiren [22], Md. Rafiqul Islma [24], Renata L. Rosa [34],Hay Mar Su Aung [74], Masum Billah [94], Rinki Chatterjee [95] Facebook Micheal M. Tadasse [16], Subhan Tariq [17], Hameed Jelodhar [54] Reddit Yuwen Lyu [13], Lixia Yu [69], Tianyi Wang [83] Weibo Li Chen Chang Youtube Badr Ait Hammou [15], Marija Stanojevic [81] Yelp Fazeel Abid [14] IMDB Anirban Mukherjee [73], Marija Stanojevic [81] Amazon…”

Section: Data Sourcementioning

confidence: 99%

Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

2021

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“…• A semi-supervised system was designed to enable labeling of large amounts of data with a small amount of labeled data. 16 In this context, a data set of 11.75 million posts was obtained to train the semi-advised system, organizations supporting gun rights and gun control were identified and 8448 posts from 171 profiles were tagged. In the study, it was stated that prejudiced thoughts about gun control/support can be classified with 95.6% accuracy.…”

Section: Sociologymentioning

confidence: 99%

Social big data applications and challenges

Erol¹,

Aksoy²,

Sağıroğlu³

2022

Concurrency and Computation

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Lifestyles of individuals have changed drastically in the last two decades with the impact of social media platforms which transforms individuals from being users into an asset of social media. The assets now become very precious and seriously attract who can generate useful or harmful values. In this context, studies conducted in the last 5 years are analyzed based on the methodology covering implementation areas, data sources, data size, methods and tools. The studies were classified and summarized under nine main "research fields," and a "purpose-based" classification under three main purposes was investigated. The results have shown that even if data obtained from social media platforms are often preferred in the studies, issues such as compliance with legal regulations, data processing, confidentiality and privacy of data also bring difficulties; collection and processing of social big data are a serious obstacle to the realization of many studies; not enough data sources provided by public or private enterprises; most of the studies carried out on text data, and the rest focused on location and image data; mostly machine learning methods are preferred in applications. This study differs from previous literature reviews by revealing comprehensively how social big data can be transformed into practice with a holistic perspective.

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Surveying public opinion using label prediction on social media data

Cited by 9 publications

References 10 publications

Stay on Topic, Please: Aligning User Comments to the Content of a News Article

Stay on Topic, Please: Aligning User Comments to the Content of a News Article

Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

Social big data applications and challenges

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