Analyzing Discourse Communities with Distributional Semantic Models

Brigadir, Igor; Greene, Derek; Cunningham, Pádraig

doi:10.1145/2786451.2786470

Cited by 20 publications

(20 citation statements)

References 20 publications

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“…Prior work has shown that language dynamics may hinder models upon deployment (Dredze et al, 2016;Huang and Paul, 2018). In social media, where users adopt new linguistic norms rapidly, performance may be more volatile (Brigadir et al, 2015).…”

Section: Temporal Transfermentioning

confidence: 99%

Do Models of Mental Health Based on Social Media Data Generalize?

Harrigian¹,

Aguirre²,

Dredze³

2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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Proxy-based methods for annotating mental health status in social media have grown popular in computational research due to their ability to gather large training samples. However, an emerging body of literature has raised new concerns regarding the validity of these types of methods for use in clinical applications. To further understand the robustness of distantly supervised mental health models, we explore the generalization ability of machine learning classifiers trained to detect depression in individuals across multiple social media platforms. Our experiments not only reveal that substantial loss occurs when transferring between platforms, but also that there exist several unreliable confounding factors that may enable researchers to overestimate classification performance. Based on these results, we enumerate recommendations for future mental health dataset construction.

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Section: Temporal Transfermentioning

confidence: 99%

Do Models of Mental Health Based on Social Media Data Generalize?

Harrigian¹,

Aguirre²,

Dredze³

2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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“…In this case, the network exhibits a highly segregated structure, with limited connectivity among the different clusters, meaning that each cluster represents a different viewpoint. Figure 1 depicts the conductance values for different graph partitioning scenarios for the controversial topic 2014 Scottish Independence Referendum, using the dataset provided in [9] which contains only supporters of "yes" and "no" (more in Section 6). We notice that for k = 2, the two clusters have very low conductance value.…”

Section: 22mentioning

confidence: 99%

Viewpoint Discovery and Understanding in Social Networks

Quraishi

Fafalios

Herder

2018

Proceedings of the 10th ACM Conference on Web Science

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The Web has evolved to a dominant platform where everyone has the opportunity to express their opinions, to interact with other users, and to debate on emerging events happening around the world. On the one hand, this has enabled the presence of different viewpoints and opinions about a − usually controversial − topic (like Brexit), but at the same time, it has led to phenomena like media bias, echo chambers and filter bubbles, where users are exposed to only one point of view on the same topic. Therefore, there is the need for methods that are able to detect and explain the different viewpoints. In this paper, we propose a graph partitioning method that exploits social interactions to enable the discovery of different communities (representing different viewpoints) discussing about a controversial topic in a social network like Twitter. To explain the discovered viewpoints, we describe a method, called Iterative Rank Difference (IRD), which allows detecting descriptive terms that characterize the different viewpoints as well as understanding how a specific term is related to a viewpoint (by detecting other related descriptive terms). The results of an experimental evaluation showed that our approach outperforms state-of-the-art methods on viewpoint discovery, while a qualitative analysis of the proposed IRD method on three different controversial topics showed that IRD provides comprehensive and deep representations of the different viewpoints.

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“…Sco ish Independence Referendum (SR) [11]. Pro les in P T were part of the Scottish Independence Referendum Electoral Commission, or unambiguously indicated their stance in their Twitter biographies, giving us Σ SR = ("Yes", "No").…”

mentioning

confidence: 99%

“…Pro les in P T were part of the Scottish Independence Referendum Electoral Commission, or unambiguously indicated their stance in their Twitter biographies, giving us Σ SR = ("Yes", "No"). [11]. Stances were determined thanks to several sources listing o cial Twitter accounts of campaigners: Σ ME = ("Democrat", "Republican").…”

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confidence: 99%

Stance Classification through Proximity-based Community Detection

Fraisier

Cabanac

Pitarch

et al. 2018

Proceedings of the 29th on Hypertext and Social Media

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Numerous domains have interests in studying the viewpoints expressed online, be it for marketing, cybersecurity, or research purposes with the rise of computational social sciences. Current stance detection models are usually grounded on the specificities of some social platforms. This rigidity is unfortunate since it does not allow the integration of the multitude of signals informing effective stance detection. We propose the SCSD model, or Sequential Community-based Stance Detection model, a semi-supervised ensemble algorithm which considers these signals by modeling them as a multi-layer graph representing proximities between profiles. We use a handful of seed profiles, for whom we know the stance, to classify the rest of the profiles by exploiting like-minded communities. These communities represent profiles close enough to assume they share a similar stance on a given subject. Using datasets from two different social platforms, containing two to five stances, we show that by combining several types of proximity we can achieve excellent results. Moreover, we compare the proximities to find those which convey useful information in term of stance detection.

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Analyzing Discourse Communities with Distributional Semantic Models

Cited by 20 publications

References 20 publications

Do Models of Mental Health Based on Social Media Data Generalize?

Do Models of Mental Health Based on Social Media Data Generalize?

Viewpoint Discovery and Understanding in Social Networks

Stance Classification through Proximity-based Community Detection

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