United States

Misinformation on social media can stem from truthful news articles that are cited in misleading ways. In contrast to traditional media, the collaborative nature of information propagation on these platforms means that facts presented on posts are known second-hand or third-hand, and as such often contain transformations of information that may be misleading or incomplete, potentially leading to a propagation of falsehoods. As a first step in analyzing misinformation occurrences in information transfer scenarios on social media, we propose the task of classifying social media posts that quote news articles from the perspective of information transformation. Specifically, the task is composed of classifying the posts based on the faithfulness of factual claims and the degree to which the posts are misleading. On the basis of the proposed annotation criteria, 200 tweets (X posts) quoting news articles on COVID-19 were labeled by 3 annotators, with the result that 20% of posts were labeled as contained potentially misleading information concerning the article by a majority of annotators. We conduct exploratory experiments leveraging large language models (LMs) to classify information transformation with few-shot prompting, with performance on misleadingness classification approaching that of some human annotators.

EMNLP 2024

Detecting Misleading News Representations on Social Media Posts

rumour verification

fact verification

covid-19

social media

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!

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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
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EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

Detecting and tracking emerging trends and weak signals in large, evolving text corpora is vital for applications such as monitoring scientific literature, managing brand reputation, surveilling critical infrastructure and more generally to any kind of text-based event detection. Existing solutions often fail to capture the nuanced context or dynamically track evolving patterns over time. BERTrend, a novel method, addresses these limitations using neural topic modeling in an online setting. It introduces a new metric to quantify topic popularity over time by considering both the number of documents and update frequency. This metric classifies topics as noise, weak, or strong signals, flagging emerging, rapidly growing topics for further investigation. Experimentation on two large real-world datasets demonstrates BERTrend's ability to accurately detect and track meaningful weak signals while filtering out noise, offering a comprehensive solution for monitoring emerging trends in large-scale, evolving text corpora. The method can also be used for retrospective analysis of past events. In addition, the use of Large Language Models together with BERTrend offers efficient means for the interpretability of trends of events.

BERTrend: Neural Topic Modeling for Emerging Trends Detection

Event detection in text streams is a crucial task for the analysis of online media and social networks. One of the current challenges in this field is establishing a performance standard while maintaining an acceptable level of computational complexity. In our study, we use an incremental clustering algorithm combined with recent advancements in sentence embeddings. Our objective is to compare our findings with previous studies, specifically those by Cao et al. (2024) and Mazoyer et al. (2020). Our results demonstrate significant improvements and could serve as a relevant baseline for future research in this area.

An Incremental Clustering Baseline for Event Detection on Twitter

Language models (LMs) show exceptional promise in the area of few-shot event extraction, but they suffer from certain limitations. In particular, DEGREE (Hsu et al., 2022) is an LM-based event extraction model that has recently been supplanted by other large language model-based state-of-the-art systems, but it suffers from an inability to cope with multiple events in the same region of an input document. In this work, we present a simple method for extending this system with the ability to gracefully handle different densities of events within documents, thereby rendering it competitive with the state-of-the-art once more, and additionally explore a novel evaluation metric that can be used to qualitatively compare the outputs of different event extraction systems. Finally, we show that our extension allows models to break apart documents into less small pieces during processing without sacrificing accuracy.

DEGREE^2: Efficient Extraction of Multiple Events Using Language Models

Information extraction has led the way for event detection from text for many years. Recent advances in neural models, such as Large Language Models (LLMs) and Vision-Language Models (VLMs), have enabled the integration of multiple modalities, providing richer sources of information about events. Concurrently, the development of schema graphs and 3D reconstruction methods has enhanced our ability to visualize and annotate complex events. Building on these innovations, we introduce the MUMOSA (MUlti-MOdal Situation Awareness) interactive dashboard that brings these diverse resources together. MUMOSA aims to provide a comprehensive platform for event situational awareness, offering users a powerful tool for understanding and analyzing complex scenarios across modalities.

MUMOSA, Interactive Dashboard for MUlti-MOdal Situation Awareness

Language models (LMs) trained on web-scale datasets are largely successful due to their ability to memorize large amounts of training data, even if only present in a few examples. These capabilities are often desirable in evaluation on tasks such as question answering but raise questions about whether these models can exhibit genuine reasoning or succeed only at mimicking patterns from the training data. This distinction is particularly salient in forecasting tasks, where the answer is not present in the training data, and the model must reason to make logical deductions. We present Reasoning and Tools for Forecasting (RTF), a framework of reasoning-and-acting (ReAct) agents that can dynamically retrieve updated information and run numerical simulation with equipped tools. We evaluate our model with questions from competitive forecasting platforms and demonstrate that our method is competitive with and can outperform human predictions. This suggests that LMs, with the right tools, can indeed think and adapt like humans, offering valuable insights for real-world decision-making.

Reasoning and Tools for Human-Level Forecasting

Document-level information extraction (doc-IE) plays a pivotal role in the realm of natural language processing (NLP). This paper embarks on a comprehensive review and discussion of contemporary literature related to doc-IE. In addition, we conduct a thorough error analysis using state-of-the-art algorithms, shedding light on their limitations and remaining challenges for tackling the task of doc-IE. Our findings demonstrate that issues like entity coreference resolution and the lack of robust reasoning significantly hinder the effectiveness of document-level information extraction (doc-IE). Additionally, we uncover new challenges, including labeling noise and relation transitivity. The overarching objective of this survey paper is to provide valuable insights that can empower NLP researchers to further advance the performance of doc-IE.

A Comprehensive Survey on Document-Level Information Extraction

This paper aims to map out the current landscape of generative approaches to the task of event extraction. In surveying the emerging literature on the topic, we identify the distinctive properties of existing studies and catalogue them to build a comprehensive view of the various techniques employed. Finally, looking ahead, we argue for a new generative formulation of event extraction, allowing for a better fit between methodology and task - a proposal that could also pertain to many other traditional NLP tasks currently based on annotations of text-spans.

Generative Approaches to Event Extraction: Survey and Outlook

We introduce a neural architecture finetuned for the task of scenario context generation: The relevant location and time of an event or entity mentioned in text. Contextualizing information extraction helps to scope the validity of automated finings when aggregating them as knowledge graphs. Our approach uses a high-quality curated dataset of time and location annotations in a corpus of epidemiology papers to train an encoder-decoder architecture. We also explored the use of data augmentation techniques during training. Our findings suggest that a relatively small fine-tuned encoder-decoder model performs better than out-of-the-box LLMs and semantic role labeling parsers to accurate predict the relevant scenario information of a particular entity or event.

When and Where Did it Happen? An Encoder-Decoder Model to Identify Scenario Context

Identifying events from text has a long past in narrative analysis, but a short history in Natural Language Processing (NLP). In this position paper, a question is asked: given the telling of a sequence of real-world events by a news narrator, what do NLP event extraction models capture, and what do they miss? Insights from critical discourse analysis (CDA) and from a series of movements in literary criticism motivate us to model the narrated logic in news narratives. As a result, a computational framework is proposed to model the function of news narration, which shapes the narrated world, consumed by news narratees. As a simplification, we represent the causal logic between events depicted in the narrated world.

Narration as Functions: from Events to Narratives

Automatic storytelling is a difficult NLP task that poses a challenge even for state-of-the-art large language models. This paper proposes a pipeline for interactive fairy tale generation in a mixed-initiative setting. Our approach introduces a story goal as a stopping condition, imposes minimal structure on the narrative in the form of a simple emotional arc, and controls the transition between the stages of the story via system prompt engineering. The resulting framework reconciles creating a structured and complete short-form narrative with retaining player agency and allowing users to influence the storyline through their input. We evaluate our approach with several proprietary and open-source language models and examine its transferability to different languages, specifically English and Russian.

Next from EMNLP 2024

BERTrend: Neural Topic Modeling for Emerging Trends Detection

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