Community detection in political Twitter networks using Nonnegative Matrix Factorization methods

Özer, Mert; Kim, Nyunsu; Davulcu, Hasan

doi:10.1109/asonam.2016.7752217

Cited by 35 publications

(21 citation statements)

References 27 publications

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“…While the use of hashtags is simple and powerful, they are not the only mechanism for coordination and organization on Twitter. During our experiments, by using data sets, which ignore tracedHT related features (Section 7.2), we aimed to test performance of our approach in case of tweets are collected based on other mechanisms such as community detection 41,42 algorithms or topic detection 43 methods.…”

Section: Discussionmentioning

confidence: 99%

Organized Behavior Classification of Tweet Sets using Supervised Learning Methods

Beğenilmiş

Uskudarli

2018

Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics

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During the 2016 US elections Twitter experienced unprecedented levels of propaganda and fake news through the collaboration of bots and hired persons, the ramifications of which are still being debated. This work proposes an approach to identify the presence of organized behavior in tweets. The Random Forest, Support Vector Machine, and Logistic Regression algorithms are each used to train a model with a data set of 850 records consisting of 299 features extracted from tweets gathered during the 2016 US presidential election. The features represent user and temporal synchronization characteristics to capture coordinated behavior. These models are trained to classify tweet sets among the categories: organic vs organized, political vs nonpolitical, and pro-Trump vs pro-Hillary vs neither. The random forest algorithm performs better with greater than 95% average accuracy and f-measure scores for each category. The most valuable features for classification are identified as user based features, with media use and marking tweets as favorite to be the most dominant.

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Section: Discussionmentioning

confidence: 99%

Organized Behavior Classification of Tweet Sets using Supervised Learning Methods

Beğenilmiş

Uskudarli

2018

Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics

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“…Moreover, DNMF [39] is proposed based on the the idea that not only the data, but also the features lie on a manifold. The graph regularizers proposed by the above methods have been utilized by several other works [30,33] to detect communities on social media. Moreover, another work [1] proposes a NMF-based approach utilizing a graph regularizer to exploit different social views (i.e., different social interactions and user-generated content) as well as prior knowledge in order to detect and profile communities.…”

Section: Nmf-based Methodsmentioning

confidence: 99%

Sentiment-driven Community Profiling and Detection on Social Media

Salehi

Özer

Davulcu

2018

Proceedings of the 29th on Hypertext and Social Media

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Web 2.0 helps to expand the range and depth of conversation on many issues and facilitates the formation of online communities. Online communities draw various individuals together based on their common opinions on a core set of issues. Most existing community detection methods merely focus on discovering communities without providing any insight regarding the collective opinions of community members and the motives behind the formation of communities. Several efforts have been made to tackle this problem by presenting a set of keywords as a community profile. However, they neglect the positions of community members towards keywords, which play an important role for understanding communities in the highly polarized atmosphere of social media. To this end, we present a sentiment-driven community profiling and detection framework which aims to provide community profiles presenting positive and negative collective opinions of community members separately. With this regard, our framework initially extracts key expressions in users' messages as representative of issues and then identifies users' positive/negative attitudes towards these key expressions. Next, it uncovers a low-dimensional latent space in order to cluster users according to their opinions and social interactions (i.e., retweets). We demonstrate the effectiveness of our framework through quantitative and qualitative evaluations.

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“…Computational approaches of Stance learningwhich involves finding people's attitude about a topic of interest -have primarily appeared in two flavors. 1) Recognizing stance in debates (Somasundaran and Wiebe, 2010;Ozer et al, 2016) 2) Conversations on online social-media platforms. Since our research focuses on conversations on social-media platforms, we discuss some important contributions here.…”

Section: Stance Learningmentioning

confidence: 99%

Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations

Kumar¹,

Carley²

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

108

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Learning from social-media conversations has gained significant attention recently because of its applications in areas like rumor detection. In this research, we propose a new way to represent social-media conversations as binarized constituency trees that allows comparing features in source-posts and their replies effectively. Moreover, we propose to use convolution units in Tree LSTMs that are better at learning patterns in features obtained from the source and reply posts. Our Tree LSTM models employ multi-task (stance + rumor) learning and propagate the useful stance signal up in the tree for rumor classification at the root node. The proposed models achieve stateof-the-art performance, outperforming the current best model by 12% and 15% on F1-macro for rumor-veracity classification and stance classification tasks respectively.

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Community detection in political Twitter networks using Nonnegative Matrix Factorization methods

Cited by 35 publications

References 27 publications

Organized Behavior Classification of Tweet Sets using Supervised Learning Methods

Organized Behavior Classification of Tweet Sets using Supervised Learning Methods

Sentiment-driven Community Profiling and Detection on Social Media

Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations

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