United Arab Emirates

We address dissonant stance detection, classifying conflicting stance between two input statements.
Computational models for traditional stance detection have typically been trained to indicate pro/con for a given target topic (e.g. gun control) and thus do not generalize well to new topics.
In this paper, we systematically evaluate the generalizability of dissonant stance detection to situations where examples of the topic have not been seen at all or have only been seen a few times.
We show that dissonant stance detection models trained on only 8 topics, none of which are the target topic, can perform as well as those trained only on a target topic. Further, adding non-target topics boosts performance further up to approximately 32 topics where accuracies start to plateau. 
Taken together, our experiments suggest dissonant stance detection models can generalize to new unanticipated topics, an important attribute for the social scientific study of social media where new topics emerge daily.

EMNLP 2022

Detecting Dissonant Stance in Social Media: The Role of Topic Exposure

topic-independent

dissonance

stance detection

workshop paper

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
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Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

While acknowledgments are often overlooked and sometimes entirely missing from publications, this short section of a paper can provide insights on the state of a field. We characterize and perform a textual analysis of acknowledgments in NLP conference proceedings across the last 17 years, revealing broader trends in funding and research directions in NLP as well as interesting phenomena including career incentives and the influence of defaults.

An Analysis of Acknowledgments in NLP Conference Proceedings

Word embedding models have been used in prior work to extract associations of intersectional identities within discourse concerning institutions of power, but restricted its focus on narratives of the nineteenth-century U.S. south. This paper leverages this prior work and introduces an initial study on the association of intersected identities with discourse concerning social institutions within social media from Nigeria. Specifically, we use word embedding models trained on tweets from Nigeria and extract associations of intersected social identities with institutions (e.g., domestic, culture, etc.) to provide insight into the alignment of identities with institutions. Our initial experiments indicate that identities at the intersection of gender and economic status groups have significant associations with discourse about the economic, political, and domestic institutions.

Extracting Associations of Intersectional Identities with Discourse about Institution from Nigeria

Data scarcity is a common problem in NLP, especially when the annotation pertains to nuanced socio-linguistic concepts that require specialized knowledge. As a result, few-shot identification of these concepts is desirable. Few-shot in-context learning using pre-trained Large Language Models (LLMs) has been recently applied successfully in many NLP tasks. In this paper, we study few-shot identification of a psycho-linguistic concept, Morality Frames (Roy et al., 2021), using LLMs. Morality frames are a representation framework that provides a holistic view of the moral sentiment expressed in text, identifying the relevant moral foundation (Haidt and Graham, 2007) and at a finer level of granularity, the moral sentiment expressed towards the entities mentioned in the text. Previous studies relied on human annotation to identify morality frames in text which is expensive. In this paper, we propose prompting based approaches using pretrained Large Language Models for identification of morality frames, relying only on few-shot exemplars. We compare our models' performance with few-shot RoBERTa and found promising results.

Towards Few-Shot Identification of Morality Frames using In-Context Learning

Previous approaches to detecting toxic language and norm violations in online conversations are primarily based on asynchronous settings, such as online forums and social media, and they are less effective as they overlook unique characteristics of conversations taking place in the synchronous setting, i.e. real-time conversations. 
In this work, we share our progress in the first NLP study on detecting norm violations in real-time conversations. 
We collect 500 moderated comments and their corresponding context from Twitch live streams and annotate them with reasons for moderation.
Then, we analyze the unique nature of real-time conversations that distinguish them from asynchronous communication 
and examine the importance of various contexts in detecting norm violations in real-time conversations. Lastly, we discuss modeling approaches that leverage our findings. 
In our experiments, we observe that existing models on asynchronous communication perform poorly in detecting norm violations in real-time conversations.
We also find that previous chat context in live streaming chat helps detect norm violations.

Analyzing Norm Violations in Real-Time Live-Streaming Chat

Sarcasm is prevalent in all corners of social media, posing many challenges within Natural Language Processing (NLP), particularly for sentiment analysis. Sarcasm detection remains a largely unsolved problem in many NLP tasks due to its contradictory and typically derogatory nature as a figurative language construct. With recent strides in NLP, many pre-trained language models exist that have been trained on data from specific social media platforms, i.e., Twitter. In this paper, we evaluate the efficacy of multiple sarcasm detection datasets using machine and deep learning models. We create two new datasets - a manually annotated gold standard Sarcasm Annotated Dataset (SAD) and a Silver-Standard Sarcasm-annotated Dataset (S3D). Using a combination of existing sarcasm datasets with SAD, we train a sarcasm detection model over a social-media domain pre-trained language model, BERTweet, which yields an F1-score of 78.29\%. Using an Ensemble model with an underlying majority technique, we further label S3D to produce a weakly supervised dataset containing over \$100,000\$ tweets. We publicly release all the code, our manually annotated and weakly supervised datasets, and fine-tuned models for further research.

Utilizing Weak Supervision to Create S3D: A Sarcasm Annotated Dataset

The Stereotype Content model (SCM) states that we tend to perceive minority groups as cold, incompetent or both. In this paper we adapt existing work to demonstrate that the Stereotype Content model holds for contextualised word embeddings, then use these results to evaluate a fine-tuning process designed to drive a language model away from stereotyped portrayals of minority groups. We find the SCM terms are better able to capture bias than demographic agnostic terms related to pleasantness. Further, we were able to reduce the presence of stereotypes in the model through a simple fine-tuning procedure that required minimal human and computer resources, without harming downstream performance. We present this work as a prototype of a debiasing procedure that aims to remove the need for a priori knowledge of the specifics of bias in the model.

A Robust Bias Mitigation Procedure Based on the Stereotype Content Model

Language models such as GPT-3 have caused a furore in the research community. Some studies found that GPT-3 has some creative abilities and makes mistakes that are on par with human behaviour. This paper answers a related question: Who is GPT-3? We administered two validated measurement tools to GPT-3 to assess its personality, the values it holds and its self-reported demographics. Our results show that GPT-3 scores similarly to human samples in terms of personality and - when provided with a model response memory - in terms of the values it holds. We provide the first evidence of psychological assessment of the GPT-3 model and thereby add to our understanding of this language model. We close with suggestions for future research that moves social science closer to language models and vice versa.

Who is GPT-3? An exploration of personality, values and demographics

Differential framing of issues can lead to divergent world views on important issues. This is especially true in domains where the information presented can reach a large audience, such as traditional and social media. Scalable and reliable measurement of such differential framing is an important first step in addressing them. In this work, based on the intuition that framing affects the tone and word choices in written language, we propose a framework for modeling the differential framing of issues through masked token prediction via large-scale fine-tuned language models (LMs). Specifically, we explore three key factors for our framework: 1) prompt generation methods for the masked token prediction; 2) methods for normalizing the output of fine-tuned LMs; 3) robustness to the choice of pre-trained LMs used for fine-tuning. Through experiments on a dataset of articles from traditional media outlets covering five diverse and politically polarized topics, we show that our framework can capture differential framing of these topics with high reliability.

Capturing Topic Framing via Masked Language Modeling

A standard measure of the influence of a research paper is the number of times it is cited. However, papers may be cited for many reasons, and citation count is not informative about the extent to which a paper affected the content of subsequent publications. We therefore propose a novel method to quantify linguistic influence in timestamped document collections. There are two main steps: first, identify lexical and semantic changes using contextual embeddings and word frequencies; second, aggregate information about these changes into per-document influence parameters by estimating a high-dimensional Hawkes process with a low-rank parameter matrix. The resulting measures of linguistic influence are predictive of \emph{future} citations. Specifically, the estimate of linguistic influence from the two years after a paper's publication is correlated with and predictive of its citation count in the following three years. This is demonstrated using an online evaluation with incremental temporal training/test splits, in comparison with a strong baseline that includes predictors for initial citation counts, topics, and lexical features.

Predicting Long-Term Citations from Short-Term Linguistic Influence

Status is widely used to incentivize user engagement online. However, visible status indicators could inadvertently bias online deliberation to favor high-status users. In this work, we design and deploy a randomized experiment on the ChangeMyView platform to quantify status biases in deliberation online. We find strong evidence of status bias: hiding status on ChangeMyView increases the persuasion rate of moderate-status users by 84% and decreases the persuasion rate of high-status users by 41% relative to the control group. We also find that the persuasive power of status is moderated by verbosity, suggesting that status is used as an information-processing heuristic under cognitive load. Finally, we find that a user's status influences the argumentation behavior of other users they interact with in a manner that disadvantages low and moderate-status users.

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An Analysis of Acknowledgments in NLP Conference Proceedings

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An Analysis of Acknowledgments in NLP Conference Proceedings

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PRESENTATIONS

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RESOURCES

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