Healthy Twitter discussions? Time will tell

• URL: http://arxiv.org/abs/2203.11261v1
• Date: Mon, 21 Mar 2022 18:43:40 GMT
• Title: Healthy Twitter discussions? Time will tell
• Authors: Dmitry Gnatyshak, Dario Garcia-Gasulla, Sergio Alvarez-Napagao, Jamie Arjona and Tommaso Venturini
• Abstract summary: We consider the use of temporal dynamic patterns as an indicator of discussion health. First we explore the types of discussions in an unsupervised manner, and then characterize these types using the concept of ephemerality. In the end, we discuss the potential use of our ephemerality definition for labeling online discourses based on how desirable, healthy and constructive they are.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Studying misinformation and how to deal with unhealthy behaviours within online discussions has recently become an important field of research within social studies. With the rapid development of social media, and the increasing amount of available information and sources, rigorous manual analysis of such discourses has become unfeasible. Many approaches tackle the issue by studying the semantic and syntactic properties of discussions following a supervised approach, for example using natural language processing on a dataset labeled for abusive, fake or bot-generated content. Solutions based on the existence of a ground truth are limited to those domains which may have ground truth. However, within the context of misinformation, it may be difficult or even impossible to assign labels to instances. In this context, we consider the use of temporal dynamic patterns as an indicator of discussion health. Working in a domain for which ground truth was unavailable at the time (early COVID-19 pandemic discussions) we explore the characterization of discussions based on the the volume and time of contributions. First we explore the types of discussions in an unsupervised manner, and then characterize these types using the concept of ephemerality, which we formalize. In the end, we discuss the potential use of our ephemerality definition for labeling online discourses based on how desirable, healthy and constructive they are.

Related papers

• A Glitch in the Matrix? Locating and Detecting Language Model Grounding with Fakepedia [57.31074448586854]
  Large language models (LLMs) have an impressive ability to draw on novel information supplied in their context. Yet the mechanisms underlying this contextual grounding remain unknown. We present a novel method to study grounding abilities using Fakepedia.
  arXiv  Detail & Related papers  (2023-12-04T17:35:42Z)
• A Taxonomy of Rater Disagreements: Surveying Challenges & Opportunities from the Perspective of Annotating Online Toxicity [15.23055494327071]
  Toxicity is an increasingly common and severe issue in online spaces. A rich line of machine learning research has focused on computationally detecting and mitigating online toxicity. Recent research has pointed out the importance of accounting for the subjective nature of this task.
  arXiv  Detail & Related papers  (2023-11-07T21:00:51Z)
• DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning [89.92601337474954]
  Pragmatic reasoning plays a pivotal role in deciphering implicit meanings that frequently arise in real-life conversations. We introduce a novel challenge, DiPlomat, aiming at benchmarking machines' capabilities on pragmatic reasoning and situated conversational understanding.
  arXiv  Detail & Related papers  (2023-06-15T10:41:23Z)
• SensePOLAR: Word sense aware interpretability for pre-trained contextual word embeddings [4.479834103607384]
  Adding interpretability to word embeddings represents an area of active research in text representation. We present SensePOLAR, an extension of the original POLAR framework that enables word-sense aware interpretability for pre-trained contextual word embeddings.
  arXiv  Detail & Related papers  (2023-01-11T20:25:53Z)
• Twitter Topic Classification [15.306383757213956]
  We present a new task based on tweet topic classification and release two associated datasets. Given a wide range of topics covering the most important discussion points in social media, we provide training and testing data. We perform a quantitative evaluation and analysis of current general- and domain-specific language models on the task.
  arXiv  Detail & Related papers  (2022-09-20T16:13:52Z)
• Extracting Large Scale Spatio-Temporal Descriptions from Social Media [1.14219428942199]
  The ability to track large-scale events as they happen is essential for understanding them and coordinating reactions in an appropriate and timely manner. We are exploring the hypothesis that this kind of data can be augmented with the ingestion of semi-structured data sources, like social media. Social media can diffuse valuable knowledge, such as direct witness or expert opinions, while their noisy nature makes them not trivial to manage.
  arXiv  Detail & Related papers  (2022-06-27T13:16:43Z)
• Color Overmodification Emerges from Data-Driven Learning and Pragmatic Reasoning [53.088796874029974]
  We show that speakers' referential expressions depart from communicative ideals in ways that help illuminate the nature of pragmatic language use. By adopting neural networks as learning agents, we show that overmodification is more likely with environmental features that are infrequent or salient.
  arXiv  Detail & Related papers  (2022-05-18T18:42:43Z)
• Automatic Evaluation and Moderation of Open-domain Dialogue Systems [59.305712262126264]
  A long standing challenge that bothers the researchers is the lack of effective automatic evaluation metrics. This paper describes the data, baselines and results obtained for the Track 5 at the Dialogue System Technology Challenge 10 (DSTC10)
  arXiv  Detail & Related papers  (2021-11-03T10:08:05Z)
• Context Dependent Semantic Parsing: A Survey [56.69006903481575]
  semantic parsing is the task of translating natural language utterances into machine-readable meaning representations. Currently, most semantic parsing methods are not able to utilize contextual information. To address this issue, context dependent semantic parsing has recently drawn a lot of attention.
  arXiv  Detail & Related papers  (2020-11-02T07:51:05Z)
• Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News [57.9843300852526]
  We introduce the more realistic and challenging task of defending against machine-generated news that also includes images and captions. To identify the possible weaknesses that adversaries can exploit, we create a NeuralNews dataset composed of 4 different types of generated articles. In addition to the valuable insights gleaned from our user study experiments, we provide a relatively effective approach based on detecting visual-semantic inconsistencies.
  arXiv  Detail & Related papers  (2020-09-16T14:13:15Z)
• Vocabulary-based Method for Quantifying Controversy in Social Media [0.0]
  We develop a method for controversy detection based primarily on the jargon used by the communities in social media. Our method dispenses with the use of domain-specific knowledge, is language-agnostic, efficient and easy to apply.
  arXiv  Detail & Related papers  (2020-01-14T17:43:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.