81 research outputs found
X-stance: A Multilingual Multi-Target Dataset for Stance Detection
We extract a large-scale stance detection dataset from comments written by
candidates of elections in Switzerland. The dataset consists of German, French
and Italian text, allowing for a cross-lingual evaluation of stance detection.
It contains 67 000 comments on more than 150 political issues (targets). Unlike
stance detection models that have specific target issues, we use the dataset to
train a single model on all the issues. To make learning across targets
possible, we prepend to each instance a natural question that represents the
target (e.g. "Do you support X?"). Baseline results from multilingual BERT show
that zero-shot cross-lingual and cross-target transfer of stance detection is
moderately successful with this approach.Comment: SwissText + KONVENS 2020. Data and code are available at
https://github.com/ZurichNLP/xstanc
Stance detection on social media: State of the art and trends
Stance detection on social media is an emerging opinion mining paradigm for
various social and political applications in which sentiment analysis may be
sub-optimal. There has been a growing research interest for developing
effective methods for stance detection methods varying among multiple
communities including natural language processing, web science, and social
computing. This paper surveys the work on stance detection within those
communities and situates its usage within current opinion mining techniques in
social media. It presents an exhaustive review of stance detection techniques
on social media, including the task definition, different types of targets in
stance detection, features set used, and various machine learning approaches
applied. The survey reports state-of-the-art results on the existing benchmark
datasets on stance detection, and discusses the most effective approaches. In
addition, this study explores the emerging trends and different applications of
stance detection on social media. The study concludes by discussing the gaps in
the current existing research and highlights the possible future directions for
stance detection on social media.Comment: We request withdrawal of this article sincerely. We will re-edit this
paper. Please withdraw this article before we finish the new versio
Capturing stance dynamics in social media: open challenges and research directions
Social media platforms provide a goldmine for mining public opinion on issues
of wide societal interest and impact. Opinion mining is a problem that can be
operationalised by capturing and aggregating the stance of individual social
media posts as supporting, opposing or being neutral towards the issue at hand.
While most prior work in stance detection has investigated datasets that cover
short periods of time, interest in investigating longitudinal datasets has
recently increased. Evolving dynamics in linguistic and behavioural patterns
observed in new data require adapting stance detection systems to deal with the
changes. In this survey paper, we investigate the intersection between
computational linguistics and the temporal evolution of human communication in
digital media. We perform a critical review of emerging research considering
dynamics, exploring different semantic and pragmatic factors that impact
linguistic data in general, and stance in particular. We further discuss
current directions in capturing stance dynamics in social media. We discuss the
challenges encountered when dealing with stance dynamics, identify open
challenges and discuss future directions in three key dimensions: utterance,
context and influence
Improved Target-specific Stance Detection on Social Media Platforms by Delving into Conversation Threads
Target-specific stance detection on social media, which aims at classifying a
textual data instance such as a post or a comment into a stance class of a
target issue, has become an emerging opinion mining paradigm of importance. An
example application would be to overcome vaccine hesitancy in combating the
coronavirus pandemic. However, existing stance detection strategies rely merely
on the individual instances which cannot always capture the expressed stance of
a given target. In response, we address a new task called conversational stance
detection which is to infer the stance towards a given target (e.g., COVID-19
vaccination) when given a data instance and its corresponding conversation
thread. To tackle the task, we first propose a benchmarking conversational
stance detection (CSD) dataset with annotations of stances and the structures
of conversation threads among the instances based on six major social media
platforms in Hong Kong. To infer the desired stances from both data instances
and conversation threads, we propose a model called Branch-BERT that
incorporates contextual information in conversation threads. Extensive
experiments on our CSD dataset show that our proposed model outperforms all the
baseline models that do not make use of contextual information. Specifically,
it improves the F1 score by 10.3% compared with the state-of-the-art method in
the SemEval-2016 Task 6 competition. This shows the potential of incorporating
rich contextual information on detecting target-specific stances on social
media platforms and implies a more practical way to construct future stance
detection tasks
Commonsense knowledge enhanced memory network for stance classification
Stance classification aims at identifying, in the text, the attitude toward the given targets as favorable, negative, or unrelated. In existing models for stance classification, only textual representation is leveraged, while commonsense knowledge is ignored. In order to better incorporate commonsense knowledge into stance classification, we propose a novel model named commonsense knowledge enhanced memory network, which jointly represents textual and commonsense knowledge representation of given target and text. The textual memory module in our model treats the textual representation as memory vectors, and uses attention mechanism to embody the important parts. For commonsense knowledge memory module, we jointly leverage the entity and relation embeddings learned by TransE model to take full advantage of constraints of the knowledge graph. Experimental results on the SemEval dataset show that the combination of the commonsense knowledge memory and textual memory can improve stance classification
Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation
Stance detection is typically framed as predicting the sentiment in a given
text towards a target entity. However, this setup overlooks the importance of
the source entity, i.e., who is expressing the opinion. In this paper, we
emphasize the need for studying interactions among entities when inferring
stances. We first introduce a new task, entity-to-entity (E2E) stance
detection, which primes models to identify entities in their canonical names
and discern stances jointly. To support this study, we curate a new dataset
with 10,619 annotations labeled at the sentence-level from news articles of
different ideological leanings. We present a novel generative framework to
allow the generation of canonical names for entities as well as stances among
them. We further enhance the model with a graph encoder to summarize entity
activities and external knowledge surrounding the entities. Experiments show
that our model outperforms strong comparisons by large margins. Further
analyses demonstrate the usefulness of E2E stance detection for understanding
media quotation and stance landscape, as well as inferring entity ideology.Comment: EMNLP'22 Main Conferenc
Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection
Stance Detection is concerned with identifying the attitudes expressed by an
author towards a target of interest. This task spans a variety of domains
ranging from social media opinion identification to detecting the stance for a
legal claim. However, the framing of the task varies within these domains, in
terms of the data collection protocol, the label dictionary and the number of
available annotations. Furthermore, these stance annotations are significantly
imbalanced on a per-topic and inter-topic basis. These make multi-domain stance
detection a challenging task, requiring standardization and domain adaptation.
To overcome this challenge, we propose opic fficient
anc etection (TESTED), consisting of a
topic-guided diversity sampling technique and a contrastive objective that is
used for fine-tuning a stance classifier. We evaluate the method on an existing
benchmark of datasets with in-domain, i.e. all topics seen and
out-of-domain, i.e. unseen topics, experiments. The results show that our
method outperforms the state-of-the-art with an average of F1 points
increase in-domain, and is more generalizable with an averaged increase of
F1 on out-of-domain evaluation while using of the training
data. We show that our sampling technique mitigates both inter- and per-topic
class imbalances. Finally, our analysis demonstrates that the contrastive
learning objective allows the model a more pronounced segmentation of samples
with varying labels.Comment: ACL 2023 (Oral
Migration Reframed? A multilingual analysis on the stance shift in Europe during the Ukrainian crisis
The war in Ukraine seems to have positively changed the attitude toward the critical societal topic of migration in Europe â at least towards refugees from Ukraine. We investigate whether this impression is substantiated by how the topic is reflected in online news and social media, thus linking the representation of the issue on the Web to its perception in society. For this purpose, we combine and adapt leading-edge automatic text processing for a novel multilingual stance detection approach. Starting from 5.5M Twitter posts published by 565 European news outlets in one year, beginning September 2021, plus replies, we perform a multilingual analysis of migration-related media coverage and associated social media interaction for Europe and selected European countries.
The results of our analysis show that there is actually a reframing of the discussion illustrated by the terminology change, e.g., from âmigrantâ to ârefugeeâ, often even accentuated with phrases such as âreal refugeesâ. However, concerning a stance shift in public perception, the picture is more diverse than expected. All analyzed cases show a noticeable temporal stance shift around the start of the war in Ukraine. Still, there are apparent national differences in the size and stability of this shift
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