VizTract: Visualization of Complex Social Networks for Easy User Perception

Akula, Ramya; Garibay, Iván

doi:10.3390/bdcc3010017

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…Machine learning offers a multitude of opportunities to monitor multi-actor engagement and AE intensities real time. Real-time churn prediction models, for example, can detect disengaged customers in their network (Akula and Garibay, 2019). This and other big data analytics methods provide exciting new tools to make sense of different manifestations of AE intensities and how they change over time on different network levels.…”

Section: Further Researchmentioning

confidence: 99%

The nature of actor engagement intensity: a classification scheme

Wang

Fehrer

et al. 2023

JOSM

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PurposeActor engagement (AE) literature shows inconsistent understandings of engagement intensity. However, a holistic picture of the nature of AE intensity is foundational to advance empirical AE models and measurement frameworks. This paper provides a nuanced understanding of what engagement intensity is and how it unfolds on different network levels.Design/methodology/approachThis conceptual study draws from a literature review and offers a comprehensive classification scheme of AE intensity. The literature review extends beyond marketing and service research and draws from the etymology of AE intensity in management and social science, specifically, the fields of student, employee and civic engagement.FindingsThe classification scheme clarifies that AE intensity at the individual level refers to actors' affective and cognitive tone and varying magnitudes (i.e. efforts, duration, activeness) of resource investments. At the dyad level, AE intensity represents relational strength, and at the network level, it refers to the degree of connectedness in the network.Research limitations/implicationsThe research reconciles conceptual inconsistencies in the AE literature. Our classification scheme goes beyond the individual actor and actor–actor dyad and offers a holistic overview of possible ways to operationalize AE intensity in networks.Practical implicationsThe classification scheme can be used as a strategic checklist to include AE intensities of individual actors (e.g. customers and employees), relationships between these actors and network connectedness, when further developing engagement measurement tools and benchmarks.Originality/valueThis is the first study providing a comprehensive understanding of AE intensity from an individual, dyadic and network perspective.

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Section: Further Researchmentioning

confidence: 99%

The nature of actor engagement intensity: a classification scheme

Wang

Fehrer

et al. 2023

JOSM

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“…Identifying sarcastic comments for images, videos, or text shared over social platforms is even more difficult as context lies with the image or the main text/comment/headline [3,4]. Sarcasm identification in online communications from social media sites, discussion forums, and e-commerce websites has become essential for fake news detection, sentiment analysis, opinion mining, and detecting of online trolls and cyberbullies [5][6][7][8]. Detecting sarcasm is a hot topic of research in current times.…”

Section: Introductionmentioning

confidence: 99%

Sarcasm Detection over Social Media Platforms Using Hybrid Auto-Encoder-Based Model

et al. 2022

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Sarcasm is a language phrase that conveys the polar opposite of what is being said, generally something highly unpleasant to offend or mock somebody. Sarcasm is widely used on social media platforms every day. Because sarcasm may change the meaning of a statement, the opinion analysis procedure is prone to errors. Concerns about the integrity of analytics have grown as the usage of automated social media analysis tools has expanded. According to preliminary research, sarcastic statements alone have significantly reduced the accuracy of automatic sentiment analysis. Sarcastic phrases also impact automatic fake news detection leading to false positives. Various individual natural language processing techniques have been proposed earlier, but each has textual context and proximity limitations. They cannot handle diverse content types. In this research paper, we propose a novel hybrid sentence embedding-based technique using an autoencoder. The framework proposes using sentence embedding from long short term memory-autoencoder, bidirectional encoder representation transformer, and universal sentence encoder. The text over images is also considered to handle multimedia content such as images and videos. The final framework is designed after the ablation study of various hybrid fusions of models. The proposed model is verified on three diverse real-world social media datasets—Self-Annotated Reddit Corpus (SARC), headlines dataset, and Twitter dataset. The accuracy of 83.92%, 90.8%, and 92.80% is achieved. The accuracy metric values are better than previous state-of-art frameworks.

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“…However, recognizing sarcasm in textual communication is not a trivial task as none of these cues are readily available. With the explosion of internet usage, sarcasm detection in online communications from social networking platforms [1,2], discussion forums [3,4], and e-commerce websites has become crucial for opinion mining, sentiment analysis, and in identifying cyberbullies, online trolls. The topic of sarcasm received great interest from Neuropsychology [5] to Linguistics [6], but developing computational models for automatic detection of sarcasm is still at its nascent phase.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Multi-Head Self-Attention Architecture for Sarcasm Detection in Social Media

Akula¹,

Garibay²

2021

Preprint

Self Cite

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Sarcasm is a linguistic expression often used to communicate the opposite of what is said, usually something that is very unpleasant with an intention to insult or ridicule. Inherent ambiguity in sarcastic expressions, make sarcasm detection very difficult. In this work, we focus on detecting sarcasm in textual conversations from various social networking platforms and online media. To this end, we develop an interpretable deep learning model using multi-head self-attention and gated recurrent units. Multi-head self-attention module aids in identifying crucial sarcastic cue-words from the input, and the recurrent units learn long-range dependencies between these cue-words to better classify the input text. We show the effectiveness of our approach by achieving state-of-the-art results on multiple datasets from social networking platforms and online media. Models trained using our proposed approach are easily interpretable and enable identifying sarcastic cues in the input text which contribute to the final classification score. We visualize the learned attention weights on few sample input texts to showcase the effectiveness and interpretability of our model.

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VizTract: Visualization of Complex Social Networks for Easy User Perception

Cited by 8 publications

References 19 publications

The nature of actor engagement intensity: a classification scheme

The nature of actor engagement intensity: a classification scheme

Sarcasm Detection over Social Media Platforms Using Hybrid Auto-Encoder-Based Model

Interpretable Multi-Head Self-Attention Architecture for Sarcasm Detection in Social Media

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