Despite the increasing popularity of the stance detection task, existing
approaches are predominantly limited to using the textual content of social
media posts for the classification, overlooking the social nature of the task.
The stance detection task becomes particularly challenging in cross-target
classification scenarios, where even in few-shot training settings the model
needs to predict the stance towards new targets for which the model has only
seen few relevant samples during training. To address the cross-target stance
detection in social media by leveraging the social nature of the task, we
introduce CT-TN, a novel model that aggregates multimodal embeddings derived
from both textual and network features of the data. We conduct experiments in a
few-shot cross-target scenario on six different combinations of
source-destination target pairs. By comparing CT-TN with state-of-the-art
cross-target stance detection models, we demonstrate the effectiveness of our
model by achieving average performance improvements ranging from 11% to 21%
across different baseline models. Experiments with different numbers of shots
show that CT-TN can outperform other models after seeing 300 instances of the
destination target. Further, ablation experiments demonstrate the positive
contribution of each of the components of CT-TN towards the final performance.
We further analyse the network interactions between social media users, which
reveal the potential of using social features for cross-target stance
detection.

本文提出了一种使用文本和网络特征进行多模态嵌入的新模型 CT-TN 来解决社交媒体中的跨目标立场检测问题，在跨目标场景下的实验结果表明，相比于现有的基线模型，CT-TN 的平均性能提高了 11% 至 21%，表明了其有效性。实验中还发现 CT-TN 在看到 300 个目标实例后能够胜过其他模型，并且网络交互分析进一步表明了利用社交特征进行跨目标立场检测具有潜力。