Sentiment-driven Community Profiling and Detection on Social Media

Salehi, Amin; Özer, Mert; Davulcu, Hasan

doi:10.1145/3209542.3209565

Cited by 8 publications

(5 citation statements)

References 40 publications

(48 reference statements)

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“…Graph-structured data can be found in many real-world scenarios, such as social media [37,38], protein-protein interaction networks [44], and citation networks [27].…”

Section: Discussionmentioning

confidence: 99%

Graph Attention Auto-Encoders

Salehi¹,

Davulcu²

2019

Preprint

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Auto-encoders have emerged as a successful framework for unsupervised learning. However, conventional auto-encoders are incapable of utilizing explicit relations in structured data. To take advantage of relations in graph-structured data, several graph autoencoders have recently been proposed, but they neglect to reconstruct either the graph structure or node attributes. In this paper, we present the graph attention auto-encoder (GATE), a neural network architecture for unsupervised representation learning on graph-structured data. Our architecture is able to reconstruct graphstructured inputs, including both node attributes and the graph structure, through stacked encoder/decoder layers equipped with self-attention mechanisms. In the encoder, by considering node attributes as initial node representations, each layer generates new representations of nodes by attending over their neighbors' representations. In the decoder, we attempt to reverse the encoding process to reconstruct node attributes. Moreover, node representations are regularized to reconstruct the graph structure. Our proposed architecture does not need to know the graph structure upfront, and thus it can be applied to inductive learning. Our experiments demonstrate competitive performance on several node classification benchmark datasets for transductive and inductive tasks, even exceeding the performance of supervised learning baselines in most cases.

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“…Graph-structured data can be found in many real-world scenarios, such as social media [37,38], protein-protein interaction networks [44], and citation networks [27].…”

Section: Discussionmentioning

confidence: 99%

Graph Attention Auto-Encoders

Salehi¹,

Davulcu²

2019

Preprint

Self Cite

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“…Sentiment analysis is a technique to label a message as either positive or negative, and it can effectively monitor users' emotions towards a topic over time. Tsugawa and Ohsaki (2015) and Salehi et al (2018) outline many of the methods to perform sentiment analysis. employ self-reported attitude scores to validate sentiment scores obtained from a comprehensive study on public opinion towards genome editing.…”

Section: Literature Reviewmentioning

confidence: 99%

Social network analysis of Twitter interactions: a directed multilayer network approach

Logan

LaCasse

Lunday

2023

Soc. Netw. Anal. Min.

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Effective employment of social media for any social influence outcome requires a detailed understanding of the target audience. Social media provides a rich repository of self-reported information that provides insight regarding the sentiments and implied priorities of an online population. Using Social Network Analysis, this research models user interactions on Twitter as a weighted, directed network. Topic modeling through Latent Dirichlet Allocation identifies the topics of discussion in Tweets, which this study uses to induce a directed multilayer network wherein users (in one layer) are connected to the conversations and topics (in a second layer) in which they have participated, with inter-layer connections representing user participation in conversations. Analysis of the resulting network identifies both influential users and highly connected groups of individuals, informing an understanding of group dynamics and individual connectivity. The results demonstrate that the generation of a topically-focused social network to represent conversations yields more robust findings regarding influential users, particularly when analysts collect Tweets from a variety of discussions through more general search queries. Within the analysis, PageRank performed best among four measures used to rank individual influence within this problem context. In contrast, the results of applying both the Greedy Modular Algorithm and the Leiden Algorithm to identify communities were mixed; each method yielded valuable insights, but neither technique was uniformly superior. The demonstrated four-step process is readily replicable, and an interested user can automate the process with relatively low effort or expense.

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“…Many variants of modularity-based community detection [16], [17] have been developed. Another well-known category includes spectral algorithms [18], [19], [20], [21] which aims to divide the network into several communities in which most of the interactions are within communities while the number of interactions across communities are minimized. Probabilist approaches [22], in which users are assigned to clusters in a probabilistic way, are also applied to the problem of community discovery.…”

Section: Related Workmentioning

confidence: 99%

Detecting Antagonistic and Allied Communities on Social Media

Salehi

Davulcu

2018

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

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Community detection on social media has attracted considerable attention for many years. However, existing methods do not reveal the relations between communities. Communities can form alliances or engage in antagonisms due to various factors, e.g., shared or conflicting goals and values. Uncovering such relations can provide better insights to understand communities and the structure of social media. According to social science findings, the attitudes that members from different communities express towards each other are largely shaped by their community membership. Hence, we hypothesize that intercommunity attitudes expressed among users in social media have the potential to reflect their inter-community relations. Therefore, we first validate this hypothesis in the context of social media. Then, inspired by the hypothesis, we develop a framework to detect communities and their relations by jointly modeling users' attitudes and social interactions. We present experimental results using three real-world social media datasets to demonstrate the efficacy of our framework.

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Sentiment-driven Community Profiling and Detection on Social Media

Cited by 8 publications

References 40 publications

Graph Attention Auto-Encoders

Graph Attention Auto-Encoders

Social network analysis of Twitter interactions: a directed multilayer network approach

Detecting Antagonistic and Allied Communities on Social Media

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