Multimodal Fake News Detection with Textual, Visual and Semantic Information

[EN] Recent years have seen a rapid growth in the number of fake news that are posted online. Fake news detection is very challenging since they are usually created to contain a mixture of false and real information and images that have been manipulated that confuses the readers. In this paper, we propose a multimodal system with the aim to di erentiate between fake and real posts. Our system is based on a neural network and combines textual, visual and semantic information. The textual information is extracted from the content of the post, the visual one from the image that is associated with the post and the semantic refers to the similarity between the image and the text of the post. We conduct our experiments on three standard real world collections and we show the importance of those features on detecting fake news.Anastasia Giachanou is supported by the SNSF Early Postdoc Mobility grant under the project Early Fake News Detection on Social Media, Switzerland (P2TIP2 181441). Guobiao Zhang is funded by China Scholarship Council (CSC) from the Ministry of Education of P.R. China. The work of Paolo Rosso is partially funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31)Giachanou, A.; Zhang, G.; Rosso, P. (2020). Multimodal Fake News Detection with Textual, Visual and Semantic Information. Springer. 30-38. https://doi.org/10.1007/978-3-030-58323-1_3S3038Boididou, C., et al.: Verifying multimedia use at MediaEval 2015. In: MediaEval 2015 Workshop, pp. 235–237 (2015)Castillo, C., Mendoza, M., Poblete, B.: Information credibility on Twitter. In: WWW 2011, pp. 675–684 (2011)Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: CVPR 2017, pp. 1251–1258 (2017)Davidson, T., Warmsley, D., Macy, M., Weber, I.: Automated hate speech detection and the problem of offensive language. In: ICWSM 2017 (2017)Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR 2009, pp. 248–255 (2009)Ghanem, B., Rosso, P., Rangel, F.: An emotional analysis of false information in social media and news articles. ACM Trans. Internet Technol. (TOIT) 20(2), 1–18 (2020)Giachanou, A., Gonzalo, J., Mele, I., Crestani, F.: Sentiment propagation for predicting reputation polarity. In: Jose, J.M., et al. (eds.) ECIR 2017. LNCS, vol. 10193, pp. 226–238. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56608-5_18Giachanou, A., Ríssola, E.A., Ghanem, B., Crestani, F., Rosso, P.: The role of personality and linguistic patterns in discriminating between fake news spreaders and fact checkers. In: Métais, E., Meziane, F., Horacek, H., Cimiano, P. (eds.) NLDB 2020. LNCS, vol. 12089, pp. 181–192. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-51310-8_17Giachanou, A., Rosso, P., Crestani, F.: Leveraging emotional signals for credibility detection. In: SIGIR 2019, pp. 877–880 (2019)He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR 2016, pp. 770–778 (2016)Huang, D., Shan, C., Ardabilian, M., Wang, Y., Chen, L.: Local binary patterns and its application to facial image analysis: a survey. IEEE Trans. Syst. Man Cybern. Part C 41(6), 765–781 (2011)Khattar, D., Goud, J.S., Gupta, M., Varma, V.: MVAE: multimodal variational autoencoder for fake news detection. In: WWW 2019, pp. 2915–2921 (2019)Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)Popat, K., Mukherjee, S., Yates, A., Weikum, G.: DeClarE: debunking fake news and false claims using evidence-aware deep learning. In: EMNLP 2018, pp. 22–32 (2018)Rashkin, H., Choi, E., Jang, J.Y., Volkova, S., Choi, Y.: Truth of varying shades: analyzing language in fake news and political fact-checking. In: EMNLP 2017, pp. 2931–2937 (2017)Shu, K., Wang, S., Liu, H.: Understanding user profiles on social media for fake news detection. In: MIPR 2018, pp. 430–435 (2018)Shu, K., Mahudeswaran, D., Wang, S., Lee, D., Liu, H.: FakeNewsNet: a data repository with news content, social context and spatialtemporal information for studying fake news on social media. arXiv:1809.01286 (2018)Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR 2016, pp. 2818–2826 (2016)Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24–54 (2010)Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018)Wang, Y., et al.: EANN: event adversarial neural networks for multi-modal fake news detection. In: KDD 2018, pp. 849–857 (2018)Zhao, Z., et al.: An image-text consistency driven multimodal sentiment analysis approach for social media. Inf. Process. Manag. 56(6), 102097 (2019)Zlatkova, D., Nakov, P., Koychev, I.: Fact-checking meets fauxtography: verifying claims about images. In: EMNLP-IJCNLP 2019, pp. 2099–2108 (2019

The Impact of Psycholinguistic Patterns in Discriminating between Fake News Spreaders and Fact Checkers

[EN] Fake news is a threat to society. A huge amount of fake news is posted every day on social networks which is read, believed and sometimes shared by a number of users. On the other hand, with the aim to raise awareness, some users share posts that debunk fake news by using information from fact-checking websites. In this paper, we are interested in exploring the role of various psycholinguistic characteristics in differentiating between users that tend to share fake news and users that tend to debunk them. Psycholinguistic characteristics represent the different linguistic information that can be used to profile users and can be extracted or inferred from users¿ posts. We present the CheckerOrSpreader model that uses a Convolution Neural Network (CNN) to differentiate between spreaders and checkers of fake news. The experimental results showed that CheckerOrSpreader is effective in classifying a user as a potential spreader or checker. Our analysis showed that checkers tend to use more positive language and a higher number of terms that show causality compared to spreaders who tend to use a higher amount of informal language, including slang and swear words.The works of Anastasia Giachanou and Daniel Oberski were funded by the Dutch Research Council (grant VI.Vidi.195.152). The work of Paolo Rosso was in the framework of the XAI-DisInfodemics project on eXplainable AI for disinformation and conspiracy detection during infodemics (PLEC2021-007681), funded by the Spanish Ministry of Science and Innovation, as well as IBERIFIER, the Iberian Digital Media Research and Fact-Checking Hub funded by the European Digital Media Observatory (2020-EU-IA0252).Giachanou, A.; Ghanem, BHH.; Rissola, EA.; Rosso, P.; Crestani, F.; Oberski, D. (2022). The Impact of Psycholinguistic Patterns in Discriminating between Fake News Spreaders and Fact Checkers. Data & Knowledge Engineering. 138:1-15. https://doi.org/10.1016/j.datak.2021.10196011513

Effectiveness of social media sentiment analysis tools with the support of emoticon/emoji

Organizations are increasingly interested in using microblogging platforms, such as Twitter, to get rapid feedback in several domains using sentiment analysis algorithms to rate, for example, whether a target audience is happy or unhappy. However, posts on microblogging platforms can differ from the source material used to train the sentiment analysis tools. For example, emojis and emoticons are increasingly employed in social media to clarify, enhance, or sometimes reverse the sentiment of a post but can be stripped out of a piece of text before it is processed. Responding to this interest, many sentiment analysis algorithms are being made available as web services, but as details of the algorithms used are not always published on the website, comparisons between web services and how well they deal with the peculiarities of microblogging posts can be difficult. To address this, a prototype web application was developed to compare the performance of nine tweet-related sentiment analysis web services and, through targeted hypotheses, to study the effect of emojis and emoticons on polarity classification. Twelve specific research test sets were created with the application, labelled by volunteers, and tested against the analysis web services with evaluation provided by two- and three-class accuracy measures. Distinct differences were found in how the web services used emoticons and emojis in assigning a positive or negative sentiment value to a tweet, with some services seeming to ignore their presence. It was found in general that web services classified polarity sensitive tweets significantly less accurately than tweets where the sentiment of the emoji/emoticon supported the sentiment of the text

Blogging the Virtual: New Geographies of Domination and Resistance In and Beyond Russia

Russia’s accelerating authoritarian turn has not ignored the internet, and in recent years, the Russian state has clamped down on internet activities that diverge from the statist line, employing a variety of strategies to dominate online spaces. Nevertheless, oppositional voices flourish on the Russian internet, taking shape in independent blogs and videos. This paper explores three political bloggers through surveillant and resistance assemblages, making sense of this contestation through an interpretation of the Deleuzian virtual that underscores the emancipatory potential of online activities for producing more egalitarian configurations, but also taking stock of the ways that these technologies have increased domination. Encompassing the blurriness between digital and corporeal spaces, the paper contributes by revealing new geographies of contestation against state strategies to dominate the Russian internet. Overlapping with but not corresponding to Russian territorial boundaries, these dynamics highlight shifting spaces of power and resistance in the increasingly illiberal world

Pathophysiology of severe COVID-19 infection: predisposing factors and evolution

<p>An extremely contagious virus SARS-CoV-2 emerged two years ago causing COVID-19 infection and resulting to the current pandemic, the most challenging public health crisis we have ever faced. The virus is transmitted through the respiratory tract, mostly by inhalation of aerosols, and although its virulence was initially focused mainly on the lower respiratory system, now through evolutionary mechanisms and mutations of the viral genome, it has been confined to the upper respiratory system. The COVID-19 disease presents a very wide spectrum of severity, ranging from subclinical infection (asymptomatic), mild symptoms, to critical cases, not rarely fatal. The severity of the disease depends on the immune response of the host. Ιn critical cases, hyperinflammation and hyperreaction processes of the innate and adaptive immune systems are observed, ultimately resulting in Cytokine Storm Syndrome and cases of hypercoagulation. Severely ill patients may develop acute lymphopenia, eosinopenia and neutrophilia, and/or rapidly increased levels of D-dimers. Characteristic complications of the disease are pneumonia and Acute Respiratory Distress Syndrome (ARDS), which represent severe and life-threatening conditions. This review will explain the precise mechanisms concerning the pathophysiology of the severe COVID-19 infection. Firstly, we will analyze the excessive immune response and the extensive inflammation that follows during the emergent condition known as Cytokine Storm Syndrome. Furthermore, we will research complications related to hypercoagulation and thrombus formation, with particular reference to the development of pulmonary embolism and the aggressive activation of the complement system. In addition, references will be made to predisposing factors, such as genetic mutations and blood groups, which have been associated with the aggravated symptomatology of the infection, as well as to mechanisms of inhibition of the immune response disposed to SARS-CoV-2, data that need further clarification. Finally, the evolutionary course of the virus from the beginning of the pandemic until today will be explained by grouping the strains of variants of concern (VOC) and mentioning the differences between them, both at the genetic level and the properties that each emerging mutation provides.</p&gt

The impact of psycholinguistic patterns in discriminating between fake news spreaders and fact checkers

Fake news is a threat to society. A huge amount of fake news is posted every day on social networks which is read, believed and sometimes shared by a number of users. On the other hand, with the aim to raise awareness, some users share posts that debunk fake news by using information from fact-checking websites. In this paper, we are interested in exploring the role of various psycholinguistic characteristics in differentiating between users that tend to share fake news and users that tend to debunk them. Psycholinguistic characteristics represent the different linguistic information that can be used to profile users and can be extracted or inferred from users’ posts. We present the CheckerOrSpreader model that uses a Convolution Neural Network (CNN) to differentiate between spreaders and checkers of fake news. The experimental results showed that CheckerOrSpreader is effective in classifying a user as a potential spreader or checker. Our analysis showed that checkers tend to use more positive language and a higher number of terms that show causality compared to spreaders who tend to use a higher amount of informal language, including slang and swear words

The impact of psycholinguistic patterns in discriminating between fake news spreaders and fact checkers

Fake news is a threat to society. A huge amount of fake news is posted every day on social networks which is read, believed and sometimes shared by a number of users. On the other hand, with the aim to raise awareness, some users share posts that debunk fake news by using information from fact-checking websites. In this paper, we are interested in exploring the role of various psycholinguistic characteristics in differentiating between users that tend to share fake news and users that tend to debunk them. Psycholinguistic characteristics represent the different linguistic information that can be used to profile users and can be extracted or inferred from users’ posts. We present the CheckerOrSpreader model that uses a Convolution Neural Network (CNN) to differentiate between spreaders and checkers of fake news. The experimental results showed that CheckerOrSpreader is effective in classifying a user as a potential spreader or checker. Our analysis showed that checkers tend to use more positive language and a higher number of terms that show causality compared to spreaders who tend to use a higher amount of informal language, including slang and swear words

Promises and pitfalls of social media data donations

Studies assessing the effects of social media use are largely based on measures of time spent on social media. In recent years, scholars increasingly ask for more insights in social media activities and content people engage with. Data Download Packages (DDPs), the archives of social media platforms that each European user has the right to download, provide a new and promising method to collect timestamped and content-based information about social media use. In this paper, we first detail the experiences and insights of a data collection of 110 Instagram DDPs gathered from 102 adolescents. We successively discuss the challenges and opportunities of collecting and analyzing DDPs to help future researchers in their consideration of whether and how to use DDPs. DDPs provide tremendous opportunities to get insight in the frequency, range, and content of social media activities, from browsing to searching and posting. Yet, collecting, processing, and analyzing DDPs is also complex and laborious, and demands numerous procedural and analytical choices and decisions

A systematic assessment of national artificial intelligence policies:perspectives from the Nordics and beyond

Abstract Echoing the evolving interest and impact of artificial intelligence on society, governments are increasingly looking for ways to strategically position themselves as both innovators and regulators in this new domain. One of the most explicit and accessible ways in which governments outline these plans is through national strategy and policy documents. We follow a systematic search strategy to identify national AI policy documents across twenty-five countries. Through an analysis of these documents, including topic modelling, clustering, and reverse topic-search, we provide an overview of the topics discussed in national AI policies and contrast the differences between countries. Furthermore, we analyse the frequency of eleven ethical principles across our corpus. Our paper outlines implications of the differences between geographical and cultural clusters in relation to the future development of artificial intelligence applications

A Method for Retrieval of Tweets About Hospital Patient Experience

Analysis of Twitter communications can capture data on hospital patient experience, and this will be more appropriate for hospital management and patient care because the data represent patients’ and carers’ experience about something as they happen. This paper reports on the development and testing of a semi-automatic method for retrieval of subsets of Twitter communications representing hospital patient experience on different topics and subtopics. Twelve main topics of discussions on patient experience have been identified. Furthermore, it has been demonstrated that it is possible to retrieve tweets on most of the topics by using pre-defined search strings comprising various terms that represent a given topic