United Arab Emirates

Fertility intentions as verbalized in surveys are a poor predictor of actual fertility outcomes, the number of children people have. This can partly be explained by the uncertainty people have in their intentions. Such uncertainties are hard to capture through traditional survey questions, although open-ended questions can be used to get insight into people&#39;s subjective narratives of the future that determine their intentions. Analyzing such answers to open-ended questions can be done through Natural Language Processing techniques. Traditional topic models (e.g., LSA and LDA), however, often fail to do since they rely on co-occurrences, which are often rare in short survey responses. The aim of this study was to apply and evaluate topic models on demographic survey data. In this study, we applied neural topic models (e.g. BERTopic, CombinedTM) based on language models to responses from Dutch women on their fertility plans, and compared the topics and their coherence scores from each model to expert judgments. Our results show that neural models produce topics more in line with human interpretation compared to LDA. However, the coherence score could only partly reflect on this, depending on the corpus used for calculation. This research is important because, first, it helps us develop more informed strategies on model selection and evaluation for topic modeling on survey data; and second, it shows that the field of demography has much to gain from adopting NLP methods.

EMNLP 2022

Understanding Narratives from Demographic Survey Data: a Comparative Study with Multiple Neural Topic Models

survey data

demography

language model

topic modeling

computational social science

Fertility intentions as verbalized in surveys are a poor predictor of actual fertility outcomes, the number of children people have. This can partly be explained by the uncertainty people have in their intentions. Such uncertainties are hard to capture through traditional survey questions, although open-ended questions can be used to get insight into people's subjective narratives of the future that determine their intentions. Analyzing such answers to open-ended questions can be done through Natural Language Processing techniques. Traditional topic models (e.g., LSA and LDA), however, often fail to do since they rely on co-occurrences, which are often rare in short survey responses. The aim of this study was to apply and evaluate topic models on demographic survey data. In this study, we applied neural topic models (e.g. BERTopic, CombinedTM) based on language models to responses from Dutch women on their fertility plans, and compared the topics and their coherence scores from each model to expert judgments. Our results show that neural models produce topics more in line with human interpretation compared to LDA. However, the coherence score could only partly reflect on this, depending on the corpus used for calculation. This research is important because, first, it helps us develop more informed strategies on model selection and evaluation for topic modeling on survey data; and second, it shows that the field of demography has much to gain from adopting NLP methods.

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.

From the start of the COVID-19 pandemic in Germany, different groups have been protesting measures implemented by different government bodies in Germany to control the pandemic. It was widely claimed that many of the offline and online protests were driven by conspiracy narratives disseminated through groups and channels on the messenger app Telegram. We investigate this claim by measuring the frequency of conspiracy narratives in messages from open Telegram chat groups of the Querdenken movement, set up to organize protests against COVID-19 restrictions in Germany. We furthermore explore the content of these messages using topic modelling. To this end, we collected 822k text messages sent between April 2020 and May 2022 in 34 chat groups. By fine-tuning a Distilbert model, using self-annotated data, we find that 8.24\% of the sent messages contain signs of conspiracy narratives. This number is not static, however, as the share of conspiracy messages grew while the overall number of messages shows a downward trend since its peak at the end of 2020. We further find a mix of known conspiracy narratives make up the topics in our topic model. Our findings suggest that the Querdenken movement is getting smaller over time, but its remaining members focus even more on conspiracy narratives.

Conspiracy Narratives in the Protest Movement Against COVID-19 Restrictions in Germany. A Long-term Content Analysis of Telegram Chat Groups.

Community-level linguistic variation is a core concept in sociolinguistics. In this paper, we use conditioned neural language models to learn vector representations for 510 online communities. We use these representations to measure linguistic variation between commu-nities and investigate the degree to which linguistic variation corresponds with social connections between communities. We find that our sociolinguistic embeddings are highly correlated with a social network-based representation that does not use any linguistic input.

Conditional Language Models for Community-Level Linguistic Variation

Studies on interpersonal conflict have a long history and contain many suggestions for conflict typology. We use this as the basis of a novel annotation scheme and release a new dataset of situations and conflict aspect annotations. We then build a classifier to predict whether someone will perceive the actions of one individual as right or wrong in a given situation. Our analyses include conflict aspects, but also generated clusters, which are human validated, and show differences in conflict content based on the relationship of participants to the author. Our findings have important implications for understanding conflict and social norms.

Understanding Interpersonal Conflict Types and their Impact on Perception Classification

Participants in political discourse employ rhetorical strategies---such as hedging, attributions, or denials---to display varying degrees of belief commitments to claims proposed by themselves or others. Traditionally, political scientists have studied these epistemic phenomena through labor-intensive manual content analysis. We propose to help automate such work through epistemic stance prediction, drawn from research in computational semantics, to distinguish at the clausal level what is asserted, denied, or only ambivalently suggested by the author or other mentioned entities (belief holders). We first develop a simple RoBERTa-based model for multi-source stance predictions that outperforms more complex state-of-the-art modeling. Then we demonstrate its novel application to political science by conducting a large-scale analysis of the Mass Market Manifestos corpus of U.S. political opinion books, where we characterize trends in cited belief holders---respected allies and opposed bogeymen---across U.S. political ideologies.

Examining Political Rhetoric with Epistemic Stance Detection

Customers are rapidly turning to social media for customer support. While brand agents on these platforms are motivated and well-intentioned to help and engage with customers, their efforts are often ignored if their initial response to the customer does not match a specific tone, style, or topic the customer is aiming to receive. The length of a conversation can reflect the effort and quality of the initial response made by a brand toward collaborating and helping consumers, even when the overall sentiment of the conversation might not be very positive. Thus, through this study, we aim to bridge this critical gap in the existing literature by analyzing language's content and stylistic aspects such as expressed empathy, psycho-linguistic features, dialogue tags, and metrics for quantifying personalization of the utterances that can influence the engagement of an interaction. This paper demonstrates that we can predict engagement using initial customer and brand posts.

Linguistic Elements of Engaging Customer Service Discourse on Social Media

Scandinavian countries are perceived as role-models when it comes to gender equality. With the advent of pre-trained language models and their widespread usage, we investigate to what extent gender-based harmful and toxic content exists in selected Scandinavian language models. We examine nine models, covering Danish, Swedish, and Norwegian, by manually creating template-based sentences and probing the models for completion. We evaluate the completions using two methods for measuring harmful and toxic completions and provide a thorough analysis of the results. We show that Scandinavian pre-trained language models contain harmful and gender-based stereotypes with similar values across all languages. This finding goes against the general expectations related to gender equality in Scandinavian countries and shows the possible problematic outcomes of using such models in real-world settings. Warning: Some of the examples provided in this paper can be upsetting and offensive.

Measuring Harmful Representations in Scandinavian Language Models

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.

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

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.

Understanding Narratives from Demographic Survey Data: a Comparative Study with Multiple Neural Topic Models

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Conspiracy Narratives in the Protest Movement Against COVID-19 Restrictions in Germany. A Long-term Content Analysis of Telegram Chat Groups.

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Understanding Narratives from Demographic Survey Data: a Comparative Study with Multiple Neural Topic Models

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Next from EMNLP 2022

Conspiracy Narratives in the Protest Movement Against COVID-19 Restrictions in Germany. A Long-term Content Analysis of Telegram Chat Groups.

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads