The domain of Aspect-based Sentiment Analysis, in which aspects are extracted, their sentiments are analyzed and sentiments are evolved over time, is getting much attention with increasing feedback of public and customers on social media. The immense advancements in the field urged researchers to devise new techniques and approaches, each sermonizing a different research analysis/question, that cope with upcoming issues and complex scenarios of Aspect-based Sentiment Analysis. Therefore, this survey emphasized on the issues and challenges that are related to extraction of different aspects and their relevant sentiments, relational mapping between aspects, interactions, dependencies and contextual-semantic relationships between different data objects for improved sentiment accuracy, and prediction of sentiment evolution dynamicity. A rigorous overview of the recent progress is summarized based on whether they contributed towards highlighting and mitigating the issue of Aspect Extraction, Aspect Sentiment Analysis or Sentiment Evolution. The reported performance for each scrutinized study of Aspect Extraction and Aspect Sentiment Analysis is also given, showing the quantitative evaluation of the proposed approach. Future research directions are proposed and discussed, by critically analysing the presented recent solutions, that will be helpful for researchers and beneficial for improving sentiment classification at aspect-level.

The existing models for multi-modal fake news detection focus mainly on capturing common similar semantics between different modalities to improve detection performance. However, they ignore the extraction of inconsistent features between these modalities. The intuitive cognition way people identify a piece of fake news is generally to discover if there are inconsistent semantics among news content itself and its comments, which could be abstracted as “comparing news image-text consistency - finding valuable comments - reasoning in-/consistency between news and comments”. Inspired by the cognitive process, we propose Human Cognition-based Consistency Inference Networks (HCCIN) to comprehensively explore consistent and inconsistent semantics for multi-modal fake news detection. Specifically, we first design cross-modal alignment layer to learn consistent semantics between textual and visual information within the multi-modal news, and then the comment clue discovery layer is devoted to ascertaining the most-concerned semantics by audiences between comments. Finally, we develop collaborative inference layer to drive news consistent semantics and the most-concerned semantics to reason and discover consistent and inconsistent information between them. Experiments on three public datasets, including Weibo, Twitter, and PHEME, reveal the superiority of our HCCIN.

The growth of social media platforms has made it easier for fake news to spread, which poses a significant threat to authoritative news outlets, politics, and public health. Manual verification of the massive amount of online information has proven to be a daunting task, which has led to the growing interest in automatic fake news detection. Some methods that rely on news text, images, external knowledge, social contexts, or propagation graphs have demonstrated good performance. In contrast to earlier studies that focused solely on the unimodal news textual information, recent works have integrated multimodal features from various granularities, such as words, visual semantic regions, and multimodal entities, to more effectively leverage news content and align with human reading habits. However, a comprehensive review of Multimodal Fake News Detection (MFND) is still lacking, prompting our aim to complement this topic. Specifically, we present a systematic taxonomy from the perspective of cross-modal interactions. We categorize existing methods into the data-based, entity-based, and knowledge-based approaches. Connections between various works are detailed when outlining representative papers. Additionally, we introduce prevalent multimodal learning methods, present accessible MFND datasets and evaluation metrics, and analyze current research results. Finally, the promising future research directions are discussed.

The recent serious cases of spreading false information have posed a significant threat to the social stability and even national security, urgently requiring all circles to respond adequately. Therefore, this survey illustrates how to fight against false information from its propagation process by (1) exploring the drivers of information infectivity from the content, media, user, structural, and temporal dimensions; (2) describing the propagation modeling approaches from macro (global), meso (community), and micro (individual) levels; and (3) discussing the governance strategies from both technical and application aspects. The potential data sources and the future directions of fighting are also given, hoping to facilitate more comprehensive solutions.

The existing approaches based on different neural networks automatically capture and fuse the multimodal semantics of news, which have achieved great success for fake news detection. However, they still suffer from the limitations of both shallow fusion of multimodal features and less attention to the inconsistency between different modalities. To overcome them, we propose multi-reading habits fusion reasoning networks (MRHFR) for multi-modal fake news detection. In MRHFR, inspired by people's different reading habits for multimodal news, we summarize three basic cognitive reading habits and put forward cognition-aware fusion layer to learn the dependencies between multimodal features of news, so as to deepen their semantic-level integration. To explore the inconsistency of different modalities of news, we develop coherence constraint reasoning layer from two perspectives, which first measures the semantic consistency between the comments and different modal features of the news, and then probes the semantic deviation caused by unimodal features to the multimodal news content through constraint strategy. Experiments on two public datasets not only demonstrate that MRHFR not only achieves the excellent performance but also provides a new paradigm for capturing inconsistencies between multi-modal news.