Zoom Out and Observe: News Environment Perception for Fake News Detection (2022)

Abstract: Fake news detection is crucial for preventing the dissemination of misinformation on social media. To differentiate fake news from real ones, existing methods observe the language patterns of the news post and “zoom in” to verify its content with knowledge sources or check its readers’ replies. However, these methods neglect the information in the external news environment where a fake news post is created and disseminated. The news environment represents recent mainstream media opinion and public attention, which is an important inspiration of fake news fabrication because fake news is often designed to ride the wave of popular events and catch public attention with unexpected novel content for greater exposure and spread. To capture the environmental signals of news posts, we “zoom out” to observe the news environment and propose the News Environment Perception Framework (NEP). For each post, we construct its macro and micro news environment from recent mainstream news. Then we design a popularity-oriented and a novelty-oriented module to perceive useful signals and further assist final prediction. Experiments on our newly built datasets show that the NEP can efficiently improve the performance of basic fake news detectors.

## introduction the wide spread of fake news on online social media has influenced public trust @xcite and poses real-world threats on politics @xcite , finance @xcite , public health (naeem and bhatti, 2020), etc. under such severe circumstances, automatically detecting fake news has been an important countermeasure in practice. besides directly observing the post's content patterns @xcite (figure 1(a)), most existing methods for fake news detection "zoom in" for finding richer post-level signal by checking user replies to the post @xcite and verifying the claim with knowledge sources @xcite (figure 1(b) ). however, these methods neglect a different line of "zooming out" to observe the external news environment where a fake news post is created and disseminated. our starting point is that a news environment, which represents recent mainstream media opinion and public attention, is an important inspiration of the fabrication of contemporary fake news. since any gains of fake news achieve only if it widely exposes and virally spreads, a fake news creator would carefully design how to improve the post's visibility and attract audiences' attention in the context (environment) of recently published news. such intentional design connects fake news with its news environment and conversely, we might find useful signals from the news environment to better characterize and detect fake news. figure 2 shows an example, where we name the whole set of recent news items the macro news environment and the event-similar subset as the post: syria announced a 48-hour ceasefire to celebrate the win over china men's national football team. horse-head statue of old summer palace comes home. hong kong has announced that all schools will be closed on thursday. syria beat china 2-1 in 2022 fifa world cup qualifier. p wu lei had a shot in the 29th minute of the first half. two pneumonic plague cases reported in beijing. snow dragon 2 sailed through 60°s for the first time. ## related work fake news detection is mostly formulated as a binary classification task where models are expected to accurately judge the given post as real or fake. existing works focus on discovering distinctive features in the post from various aspects as figure 2 shows, which we roughly group them as: post-only methods aim at finding shared patterns in appearances across fake news posts (figure 1(a) ). text-based studies focus on better constructing features based on sentiment @xcite , writing style @xcite , language use @xcite , discourse @xcite , etc. other works rely on deep neural models to encode contents and handle certain scenarios, such as visual-based @xcite , multi-modal @xcite and multi-domain @xcite detection. our nep provides additional news environmental information and can coordinate with post-only methods (will show in section 4). ## proposed method figure 3 overviews our proposed framework nep, whose goal is to empower fake news detectors with the effective perception of news environments. given a post p, we first construct its macro and micro environment (macroenv and microenv) using recent news data. then we model the postenvironment relationships to generate environmentperceived vectors v p,mac and v p,mic . finally, the two vectors are fused with post representation o derived from the fake news detector to predict if p is real or fake. ## experiment we conduct experiments to answer the following evaluation questions: ## discussion in practical systems evaluation on skewed online data. we tested bert-emo and bert-emo+nep on a dump of seven-month data from a chinese fake news detection system. different from offline datasets, this real-world set is highly skewed (30,977 real vs. 309 fake, roughly 100:1). 11 under such skewed circumstance, some metrics we used in tables 2 and 3 could hardly show the differences of performances among models (e.g., a model predicting all samples as real will have an incredible accuracy of 0.990). here, we report macro f1 and standardized partial auc with false positive rate of at most 0.1 (spauc @xmath0.1 , mcclish, 1989, see appendix c for the calculation detail) under different real/fake ratios (from 10:1 to 100:1). as shown in figure 7 , nep brings relative improvements of 16.89% and 5.20% in macf1 and spauc @xmath1.1 , showing its effectiveness in skewed, real scenarios. friendliness to practical systems. the nep is not only a new direction for fake news detection but also inherently friendly to practical systems: 1) timeliness. our nep works instantly as it only requires the post and mainstream news published a few days before. in practice, a system would not figure 7 : macro f1s and spaucs on the online data in different real/fake ratios. we sampled 100 times from the 100:1 set for each fo the first nine ratios. shadows show the standard deviations. the percentages denote relative improvements using the nep. construct the required collection on demand but prepare it ahead by maintaining a queue of news items. 2) compatibility. our perception module can be integrated with existing methods, which we validated on six representative ones ( ## conclusion and future work we proposed the nep to observe news environments for fake news detection on social media. we designed popularity-and novelty-oriented perception modules to assist fake news detectors. experiments on offline and online data show the effectiveness of nep in boosting the performance of existing models. we drew insights on how nep help to interpret the contribution of macro and micro environment in fake news detection. as this is the first work on the role of news environments for fake news detection, we believe further exploration is required for a deeper understanding of the effects of news environments and beyond. in the future, we plan to explore: 1) including historical news or background to handle posts weakly related to the present environment; 2) modeling post-environment relationships with diverse similarity metrics or even from other perspectives; 3) investigating the effects of different news environments (e.g., biased vs. neutral ones) to make the environment construction more principled; 4) extending this type of methodology from the text-only detection to multi-modal and social graph-based detection.