F ake F low: Fake News Detection by Modeling the Flow of Affective Information (2021)

Abstract: Fake news articles often stir the readers’ attention by means of emotional appeals that arouse their feelings. Unlike in short news texts, authors of longer articles can exploit such affective factors to manipulate readers by adding exaggerations or fabricating events, in order to affect the readers’ emotions. To capture this, we propose in this paper to model the flow of affective information in fake news articles using a neural architecture. The proposed model, FakeFlow, learns this flow by combining topic and affective information extracted from text. We evaluate the model’s performance with several experiments on four real-world datasets. The results show that FakeFlow achieves superior results when compared against state-of-the-art methods, thus confirming the importance of capturing the flow of the affective information in news articles.

## introduction in today's information landscape, fake news are used to manipulate public opinion @xcite by reshaping readers' opinions regarding some issues. in order to achieve this goal, authors of fake news' narratives need to capture the interest of the reader. thus, they are putting efforts to make their news articles look more objective and realistic. this is usually done by adding misleading terms or events that can have a negative or positive impact on the readers' emotions. short text false information, e.g., fake claims or misleading headlines, might be less harmful than news articles. they may have some eye-catching terms that aim to manipulate the readers' emotions @xcite . in many cases, the identification of this kind of exaggeration in short statements can unmask the fabrication. on the other hand, in fake news articles the authors exploit the length of the news to conceal their fabricated story. this fact exposes the readers to be emotionally manipulated while reading longer texts that have several imprecise or fabricated plots. the flow of information has been investigated for different tasks: @xcite studied the emotional arcs in stories in order to understand complex emotional trajectories; @xcite model the flow of emotions over a book and quantify its usefulness for predicting success in books; @xcite explore the problem of creating tags for movies from plot synopses using emotions. unlike previous works @xcite @xcite that discarded the chronological order of events in news articles, in this work we propose a model that takes into account the affective changes in texts to detect fake news. we hypothesize that fake news has a different distribution of affective information across the text compared to real news, e.g. more fear emotion in the first part of the article or more overall offensive terms, etc. therefore, modeling the flow of such information may help discriminating fake from real news. our model consists of two main sub-modules, topic-based and affective information detection. we combine these two sub-modules since a news article's topic may have a correlation with its affective information. for example, a fake news article about islam or black people is likely to provoke fear and express negative sentiment while another fake news that is in favor of a particular politician might try to evoke more positive emotions and also express some exaggerations. the contributions of our work are as follows: ## related work previous work on fake news detection is mainly divided into two main lines, namely with a focus on social media @xcite @xcite or online news articles @xcite @xcite . in this work we focus on the latter one. factchecking @xcite @xcite is another closely related research topic. however, fact-checking targets only short texts (that is, claims) and focuses on using external resources (e.g. web, knowledge sources) to verify the factuality of the news. the focus in previous work on fake news detection is mainly on proposing new feature sets. horne and adali (2017) present a set of content-based features, including readability (number of unique words, smog readability measure, etc.), stylistic (frequency of partof-speech tags, number of stop words, etc.) and psycholinguistic features (i.e., several categories from the liwc dictionary (tausczik and pennebaker, 2010)). when these features are fed into a support vector machine (svm) classifier and applied, for instance, to the task of distinguishing satire from real news, they obtain high accuracies. using the same features for the task of fake news detection, however, results in somewhat lower scores. pérez-rosas et al. (2018) propose a model (fakenewsdetector) that uses a feature set consisting of unigrams and bigrams, psycholinguistic, readability, punctuation and dependency-based syntactic features, and they evaluate the performance of their model in a cross-domain experiment. rashkin et al. (2017) use a model based on ngram features with a max-entropy classifier and apply it to a dataset with different types of fake news articles (e.g., satire, @xcite . similar to the previous work, the authors evaluate their system's performance on in-domain and out-of-domain test sets, respectively. news, and in particular fake news, are dynamic in nature and change constantly. in order to approach the dynamic nature of news, @xcite propose a topic-agnostic model (top-icagnostic) that is based on morphological (count of part-of-speech tags), psycholinguistic (personal concerns, affection, and perception categories from the liwc dictionary), readability (gunning fog metric, etc.) and web-markup features to capture patterns of the web pages' layout (frequency of advertisements, presence of an author name, etc.). all of the morphological, psycholinguistic and readability features in the topicagnostic model were extracted from headlines and texts of the news articles. the approach obtains a better performance than fakenewsdetector on three different datasets using a svm classifier. fakenewstracker @xcite is a deep neural network-based model that consists of two branches: one encodes news article texts and the other encodes social media engagements (e.g., tweets and their replies). a similar model called emotionally infused network (ein) is proposed in @xcite . ein encodes the text of the article and their affective content, based on several dictionaries, and then combines the two vector representations. the authors evaluate their model on a multi-class false information dataset and show the effectiveness of using emotion features extracted from the text. despite the large variety of features and models that have been explored in previous work, none of these works considers the sequence of affective information in text; instead, they feed the entire news articles as one segment into their models. in contrast, the aim of our work is to evaluate this source of information, using a neural architecture. ## the fakeflow model given an input document, the fakeflow model first divides it into n segments. then it uses both word embeddings and other affective features such as emotions, hyperbolic words, etc. in a way to catch the flow of emotions in the document. the model learns to pay attention to the flow of affective information throughout the document, in order to detect whether it is fake or real. figure 1 shows the architecture of the fakeflow model. the neural architecture has two main modules: the first module uses a convolutional neural network (cnn) to extract topic-based information from articles (left branch). the second module models the flow of the affective information within the articles via bidirectional gated recurrent units (bi-grus) (right branch). ## fake news datasets despite the recent efforts for debunking online fake news, there is a dearth of publicly available datasets. most of the available datasets are small in size (e.g., the politifact foot_4 dataset in @xcite has ∼700 available articles, the celebrity dataset in (pérez-rosas et al., 2018) has ∼500 articles, etc.), their test parts have not been manually annotated, or have been collected from a very small number of news sources. nonetheless, we evaluate fakeflow on three different available datasets to demonstrate its performance. in addition, we create our own dataset. multisourcefake: we rely on different resources for creating the training and test portions of the dataset, so as to provide a challenging benchmark. for the training part, we use opensources.co (os), mediabiasfactcheck.com (mbfc), and poli-tifact foot_5 news websites' lists. os list contains 560 domains, mbfc list has 548 domains, and the poli-tifact list has 227 domains. these lists have been annotated by professional journalists. the lists contain domains of online news websites annotated based on the content type (as in the os news list: satire, reliable, etc.; and in the politifact news list: imposter, parody, fake news, etc.) or from a factuality perspective (as in the mbfc news list: low, medium, and high factuality). from the os list, we select domains that are in one of the following categories: fake, bias, reliable, hate, satire, or conspiracy. we consider domains under the reliable category as real news sources, and the rest as fake. the politifact list is different from the os list since it has only labels for domains that are either fake or with mixed content. we discard the mixed ones foot_6 and map the remaining ones to the fake news label. finally, we select from the mbfc list those domains that are annotated either as high or low factual news and we map them to real and fake labels, respectively. out of these three final lists, we select only those domains for our dataset that are annotated in all lists in a consistent way; for example, we discard those domains that are annotated as real in the os list but their label in the mbfc list is fake (low factuality). the final list contains 85 news websites. we now proceed by projecting the domain-level ground truth onto the content of those domains and randomly sample articles, with a maximum of 100 news articles per domain. 8 for the test part, we use the leadstories.com fact checking website for which professional journalists annotated online news articles on the article level as fake or real. we do not follow the way we annotate the training part since the projection of the domainlevel ground truth inevitably introduces noise. the journalists that annotated leadstories.com assigned a set of labels to the fake news articles like, e.g., false, no evidence, satire, misleading, etc.; we map them all to the fake label. in addition, we discard all articles that are multimedia-based. after collecting the news articles, we postprocess them by discarding very short articles (less than 30 words). the test part includes 689 fake news articles. we complement the set with a sample of 1,000 real news articles from the training part. the overall dataset consists of 5,994 real and 5,403 fake news articles. the average document length (number of words) in the multisourcefake dataset is 422 words, and the 95th percentile value is 942. figure 2 shows the distribution of the documents' length in the dataset. truthshades: this dataset has been proposed in @xcite . the dataset was crawled from a set of domains that are annotated by professional journalists as either propaganda, hoax, satire, or real. the dataset has been built from the english gigaword corpus for real news, and other seven unreliable domains that annotated in one of the three previous false information labels. politicalnews: due to the fact that: "a classifier trained using content from articles published at a given time is likely to become ineffective in the future" @xcite , the authors of this work collected a dataset by crawling news websites in between the years 2013 to 2018 in order to evaluate their model's performance on different years. fakenewsnet: is a fake news repository that consists of two comprehensive datasets, one collected using claims from politifact and the other from the gossipcop fact checking website. given the large number of true and false claims from these two fact checking websites, @xcite built news datasets that contain visual and textual news articles content and social media information by searching twitter for users who shared news. out of the whole collected information, we use only the textual information of news articles, which is the part we are interested in. baselines. to evaluate the performance of our model, we use a combination of fake news detection models and deep neural network architectures: ## results and analysis our best result was achieved by using 10 as the number of segments (n , as found on the validation data). in figure 3 we show the model's performance for segments of different length. 11 in general, the results show that models that are based on either word ngrams or word embeddings are performing better than other models that use handcrafted features, e.g. @xcite . also, despite the huge amount of data used to train the bert model, the results show that bert performs worse than fakeflow and also fails to outperform some of the other models. we speculate that this is due to the fact that the input length in bert is limited to 512 words, as we mentioned previously, and a large portion of the news articles in the mul-tisourcefake dataset has a length greater than 512 words. the results of the longformer model confirm our claim regarding the documents' length and show a significantly higher f1 score than the bert model. this emphasizes that despite the strong performance of bert on multiple nlp benchmarks, it is unable to handle long text documents, in contrast, e.g., to vanilla text categorization @xcite . in addition, longformer's results show a higher f1 score than the fakeflow model, yet, the difference is statically insignificant. to isolate the contribution of topical vs. affective information we run two simplified versions of our architecture, each consisting of the networks to capture topical and affective information only. the results show that the flow of the affect information has a weak performance when used alone; this emphasizes that affective information of a news article is a meaningful, yet complementary source of information. performance on multiple datasets. in f1 on both test sets. in a similar way, the results on the politicalnews dataset show that fakeflow also outperforms the topicagnostic model, although the gap in results is not very large. finally, regarding the fakenewsnet dataset, it looks that the deep learning-based model (fakenewstracker) does not achieve a good performance comparing to the other proposed baseline by the authors, which is a logistic regression (lr) classifier with one-hot vectors of the news articles' text. furthermore, it seems that a simple word-based model works better than a more sophisticated model that incorporates social media and context information. the fakeflow model, on the other hand, achieves a better result, outperforming both the fakenewstracker and the lr baseline. topic-aware model. constantly, new events are covered by news agencies. these events are different from the old ones in terms of discourse and topic. therefore, a fake news detector trained on news articles from years back is unable to detect recent news. in this experiment, we are evaluating our approach on the politicalnews dataset that is constructed from news distributed across different years (2013 to 2018). following the experimental setup in @xcite , we train the fake-flow model on news from one year and test on the other years, one year at a time for testing. for example, we train the model on news from 2013 and we test on news from 2015. note that each test set is associated with 5 results, one for each year. figure 4 shows the average accuracy for each test set. we compare fakeflow to the topicagnostic model that proved to be effective at detecting fake news from different years. it is worth mentioning that the features of the topicagnostic model have been extracted from both headlines and text of the news articles. however, the results show that both models have a similar performance, except for the 2013 test set where fakeflow achieves a higher accuracy with a difference of 7%. the experiment shows that fakeflow is capable of detecting fake news from different years, with a flat performance across the years. attention weights. the proposed fakeflow model shows that taking into account the flow of affective information in fake news is an important perspective for fake news detection. we argue that being able to better understand the behaviour of the model can make it more transparent to the endusers. figure 5 illustrates this by showing the attention weights of a fake news article across the 10 segments (left bar). 12 the figure shows that fake-flow attends more to the beginning of the article. for better understanding, we match the affective information with the attention weights. regarding the news text in the figure, the emotions features 13 show a clear example of how fake news articles try to manipulate the reader. it looks as if the existence of fear, sadness, and surprise emotions at the beginning of the article have triggered the attention on this part. towards the end of the article, on the other hand, we can notice that such negative emotions do not exist, while emotions like joy and anticipation appear. this exemplifies how fake news try to attract the readers' attention in the first part of the text. regarding the morality features, we only match the word "kill" with the harm category. also, for the hyperbolic feature, we match the words "terrifying" and "powerful". in the same manner, both morality and hyperbolic features match words that occur at the beginning of the article. lastly, for both sentiment and im12 we averaged the attention weight matrix along the timesteps (number of segments) representations. 13 words with multiple colors mean that they have been annotated with multiple emotion types in the nrc lexicon. ## conclusion in this paper we presented fakeflow, a model that takes into account the flow of affective information (emotions, sentiment, hyperbolic words, etc.) in texts to better detect fake news articles. the model receives as input a text, segmented into smaller units, instead of processing one long fragment. this enables it to learn the flow of affective information by modeling the interaction between the topic and affective terms in the news article. we evaluated our model on four different datasets and compared it to several strong baselines. the extensive experiments show the effectiveness of fakeflow over state-of-the-art models. although fakeflow was trained using a limited amount of text, the results demonstrated that it achieves results on-par with resource-hungry models (e.g. bert and longformer). in future work, we plan to extend our dataset and study more fine-grained news types, e.g. propaganda, from an emotional perspective. moreover, we plan to investigate how we can replace the lexicon-based information with language-independent approaches in an attempt to make our model multilingual. ), the average value in the last segment (µ last seg. ), the average value in the all 10 segments (µ all seg. ), and the standard deviation (σ all seg. ) of a feature across the 10 segments, both in real and fake news.