Short Courses of the 28th Brazilian Symposium on Multimedia Systems and the Web

AGGARWAL, A.; MITTAL, M.; BATTINENI, G. Generative adversarial network: An overview of theory and applications. International Journal of Information Management Data Insights, Elsevier, v. 1, n. 1, p. 100004, 2021. https://doi.org/10.1109/TKDE.2005.99

Aggarwal, C. C. and Zhai, C. (2012). A survey of text clustering algorithms. In Mining text data, pages 77–128. Springer.

Ahmed, A., Shervashidze, N., Narayanamurthy, S., Josifovski, V. e Smola, A. J. (2013). Distributed large-scale natural graph factorization. Em Proceedings of the 22nd International Conference on World Wide Web, p. 37–48. https://doi.org/10.1146/annurev.soc.22.1.213

Aiello, L. M., Petkos, G., Martin, C., Corney, D., Papadopoulos, S., Skraba, R., Göker, A., Kompatsiaris, I., and Jaimes, A. (2013). Sensing trending topics in twitter. IEEE Transactions on multimedia, 15(6):1268–1282.

Akhtar, S., Basile, V., and Patti, V. (2019). A new measure of polarization in the annotation of hate speech. In International Conference of the Italian Association for Artificial Intelligence, pages 588–603. Springer.

Akoglu, L. (2014). Quantifying political polarity based on bipartite opinion networks. In Proceedings of the International AAAI Conference on Web and Social Media, volume 8, pages 2–11.

Al Amin, M. T., Aggarwal, C., Yao, S., Abdelzaher, T., and Kaplan, L. (2017). Unveiling polarization in social networks: A matrix factorization approach. In IEEE INFOCOM2017-IEEE Conference on Computer Communications, pages 1–9. IEEE.

Al-Ayyoub, M., Rabab’ah, A., Jararweh, Y., Al-Kabi, M. N., and Gupta, B. B. (2018). Studying the controversy in online crowds’ interactions. Applied Soft Computing, 66:557–563.

Alatawi, F., Cheng, L., Tahir, A., Karami, M., Jiang, B., Black, T. e Liu, H. (2021). A survey on echo chambers on social media: Description, detection and mitigation. arXiv preprint arXiv:2112.05084.

Aldayel, A. and Magdy, W. (2019). Assessing sentiment of the expressed stance on social media. In Weber, I., Darwish, K. M., Wagner, C., Zagheni, E., Nelson, L., Aref, S., and Flöck, F., editors, Social Informatics, pages 277–286, Cham. Springer International Publishing.

ALDayel, A. and Magdy, W. (2021). Stance detection on social media: State of the art and trends. Information Processing and Management, 58(4).

Alduaiji, N., Datta, A. e Li, J. (2018). Influence propagation model for clique-based community detection in social networks. IEEE Transactions on Computational Social Systems, 5(2):563–575.

Alghamdi, R. and Alfalqi, K. (2015). A survey of topic modeling in text mining. Int. J. Adv. Comput. Sci. Appl.(IJACSA), 6(1).

Almerekhi, H., Kwak, H., Salminen, J., and Jansen, B. J. (2020). Are These Comments Triggering? Predicting Triggers of Toxicity in Online Discussions, page 3033–3040. Association for Computing Machinery, New York, NY, USA.

Almerekhi, H., Kwak, H., Salminen, J., and Jansen, B. J. (2020). Are these comments triggering? predicting triggers of toxicity in online discussions. In Proceedings of The Web Conference 2020, pages 3033–3040.

Alsini, A., Datta, A. e Huynh, D. Q. (2020). On utilizing communities detected from social networks in hashtag recommendation. IEEE Transactions on Computational Social Systems, 7(4):971–982.

ALZANTOT, M.; CHAKRABORTY, S.; SRIVASTAVA, M. Sensegen: A deep learning architecture for synthetic sensor data generation. In: IEEE. 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). [S.l.], 2017. p. 188–193.

AMIRIAN, J.; HAYET, J.-B.; PETTRÉ, J. Social ways: Learning multi-modal distributions of pedestrian trajectories with gans. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. [S.l.: s.n.], 2019. p. 0 – 0.

Arora, S. D., Singh, G. P., Chakraborty, A., and Maity, M. (2022). Polarization and social media: A systematic review and research agenda. Technological Forecasting and Social Change, 183:121942.

Artetxe, M. and Schwenk, H. (2019). Massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond. Transactions of the Association for Computational Linguistics, 7:597–610.

Ayed, S. B., Trichili, H., and Alimi, A. M. (2015). Data fusion architectures: A survey and comparison. In 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA), pages 277–282. IEEE.

Babaei, M., Kulshrestha, J., Chakraborty, A., Benevenuto, F., Gummadi, K. P., and Weller, A. (2018). Purple feed: Identifying high consensus news posts on social media. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pages 10–16.

BAGNALL, A. et al. The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data mining and knowledge discovery, Springer, v. 31, n. 3, p. 606–660, 2017.

Bail, C. A., Argyle, L. P., Brown, T. W., Bumpus, J. P., Chen, H., Hunzaker, M. F., Lee, J., Mann, M., Merhout, F. e Volfovsky, A. (2018). Exposure to opposing views on social media can increase political polarization. Proceedings of the National Academy of Sciences, 115(37):9216–9221.

Bakshy, E., Messing, S. e Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on facebook. Science, 348(6239):1130–1132. https://doi.org/10.1177%2F1354856507084420

Bakshy, E., Messing, S., and Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on facebook. Science, 348(6239):1130–1132.

Balage Filho, P., Pardo, T. A. S., and Aluísio, S. (2013). An evaluation of the brazilian portuguese liwc dictionary for sentiment analysis. In Proceedings of the 9th Brazilian Symposium in Information and Human Language Technology.

Barabási, A.-L. (2016). Network Science. "Cambridge University Press".

Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A. e Bonneau, R. (2015). Tweeting from left to right: Is online political communication more than an echo chamber? Psychological science, 26(10):1531–1542.

Barbier, G. and Liu, H. (2011). Data mining in social media. In Social network data analytics, pages 327–352. Springer.

Barros, M. F., Ferreira, C. H., Santos, B. P. d., Júnior, L. A., Mellia, M., and Almeida, J. M. (2021). Understanding mobility in networks: A node embedding approach. arXiv preprint arXiv:2111.06161.

Batrinca, B. and Treleaven, P. C. (2015). Social media analytics: a survey of techniques, tools and platforms. Ai & Society, 30(1):89–116.

Baumann, F., Lorenz-Spreen, P., Sokolov, I. M. e Starnini, M. (2020). Modeling echo chambers and polarization dynamics in social networks. Phys. Rev. Lett., 124:048301.

BEERAM, S. R.; KUCHIBHOTLA, S. Time series analysis on univariate and multivariate variables: a comprehensive survey. Communication Software and Networks, Springer, p. 119–126, 2021.

Beigman Klebanov, B., Beigman, E., and Diermeier, D. (2010). Vocabulary choice as an indicator of perspective. In Proceedings of the ACL 2010 Conference Short Papers, pages 253–257, Uppsala, Sweden. Association for Computational Linguistics.

Belcastro, L., Cantini, R., Marozzo, F., Talia, D., and Trunfio, P. (2020). Learning political polarization on social media using neural networks. IEEE Access, 8:47177–47187.

Belkin, M. e Niyogi, P. (2001). Laplacian eigenmaps and spectral techniques for embedding and clustering. Advances in Neural Information Processing Systems, 14.

BENGIO, Y.; SIMARD, P.; FRASCONI, P. Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, IEEE, v. 5, n. 2, p. 157–166, 1994.

Bessi, A. (2016). Personality traits and echo chambers on facebook. Computers in Human Behavior, 65:319–324. https://doi.org/10.1145/2181037.2181040

Bessi, A., Zollo, F., Del Vicario, M., Scala, A., Caldarelli, G. e Quattrociocchi, W. (2015). Trend of narratives in the age of misinformation. PloS one, 10(8):e0134641.

Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., and Riboni, D. (2010). A survey of context modelling and reasoning techniques. Pervasive and mobile computing, 6(2):161–180.

Bird, S. (2006). Nltk: the natural language toolkit. In Proceedings of the COLING/ACL on Interactive presentation sessions. Association for Computational Linguistics.

Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent dirichlet allocation. the Journal of machine Learning research, 3:993–1022.

Bojanowski, P., Grave, E., Joulin, A., and Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5:135–146.

Borah, A. and Singh, S. R. (2022). Investigating political polarization in India through the lens of Twitter. Social Network Analysis and Mining, 12(1):1–26.

Bougie, R., Pieters, R., and Zeelenberg, M. (2003). Angry customers don’t come back, they get back: The experience and behavioral implications of anger and dissatisfaction in services. J. of the acad. of mark. science, 31(4):377–393.

Bowman, S. R., Angeli, G., Potts, C., and Manning, C. D. (2015). A large annotated corpus for learning natural language inference. arXiv preprint arXiv:1508.05326.

BOX, G. E. et al. Time series analysis: forecasting and control. [S.l.]: John Wiley & Sons, 2015.

Boxell, L., Gentzkow, M., and Shapiro, J. M. (2017). Greater internet use is not associated with faster growth in political polarization among us demographic groups. Proceedings of the National Academy of Sciences, 114(40):10612–10617.

Brandes, U. (2008). On variants of shortest-path betweenness centrality and their generic computation. Social networks, 30(2):136–145.

BRASIL. Lei Geral de Proteção de Dados (LGPD). 2018. [link]. Acessado em: 13 Ago. 2021.

Bright, J. (2017). Explaining the emergence of echo chambers on social media: the role of ideology and extremism. Available at SSRN 2839728.

BROCK, A.; DONAHUE, J.; SIMONYAN, K. Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096, 2018.

BROCKWELL, P. J.; DAVIS, R. A. Time series: theory and methods. [S.l.]: Springer Science & Business Media, 2009.

Burt, R. S. (2003). The social structure of competition. Networks in the knowledge economy, 13:57–91.

Canales, L. and Martínez-Barco, P. (2014). Emotion detection from text: A survey. In Proceedings of the Workshop on Natural Language Processing in the 5th Information Systems Research Working Days (JISIC), pages 37–43, Quito, Ecuador. Association for Computational Linguistics.

Cao, S., Lu, W. e Xu, Q. (2015). Grarep: Learning graph representations with global structural information. Em Proceedings of the 24th ACM International on Conference on Information and Knowledge management, p. 891–900.

Cao, S., Lu, W. e Xu, Q. (2016). Deep neural networks for learning graph representations. Em Proceedings of the AAAI Conference on Artificial Intelligence, volume 30.

CARLINI, N. et al. The secret sharer: Evaluating and testing unintended memorization in neural networks. In: 28th USENIX Security Symposium (USENIX Security 19). [S.l.: s.n.], 2019. p. 267–284.

Carvalho, F., Rodrigues, R. G., Santos, G., Cruz, P., Ferrari, L., and Guedes, G. P. (2019). Evaluating the brazilian portuguese version of the 2015 liwc lexicon with sentiment analysis in social networks. In Anais do BRASNAM.

Cer, D., Diab, M., Agirre, E., Lopez-Gazpio, I., and Specia, L. (2017). Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation. arXiv preprint arXiv:1708.00055.

Cer, D., Yang, Y., Kong, S.-y., Hua, N., Limtiaco, N., John, R. S., Constant, N., Guajardo-Cespedes, M., Yuan, S., Tar, C., et al. (2018). Universal sentence encoder. arXiv preprint arXiv:1803.11175.

CHATFIELD, C. The analysis of time series: an introduction. [S.l.]: Chapman and Hall/CRC, 2003.

CHEN, D. et al. Gan-leaks: A taxonomy of membership inference attacks against generative models. In: Proceedings of the 2020 ACM SIGSAC conference on computer and communications security. [S.l.: s.n.], 2020. p. 343–362.

CHEN, J. et al. Generative dynamic link prediction. Chaos: An Interdisciplinary Journal of Nonlinear Science, AIP Publishing LLC, v. 29, n. 12, p. 123111, 2019.

Chidambaram, M., Yang, Y., Cer, D., Yuan, S., Sung, Y.-H., Strope, B., and Kurzweil, R. (2018). Learning cross-lingual sentence representations via a multi-task dual-encoder model. arXiv preprint arXiv:1810.12836.

Chitra, U. e Musco, C. (2020). Analyzing the impact of filter bubbles on social network polarization. Em Proceedings of the 13th International Conference on Web Search and Data Mining, p. 115–123.

Choi, Y., Jung, Y., and Myaeng, S.-H. (2010). Identifying controversial issues and their sub-topics in news articles. In Pacific-Asia Workshop on Intelligence and Security Informatics, pages 140–153. Springer.

CHOLLET, F. Deep learning with Python. [S.l.]: Simon and Schuster, 2021.

Churchill, R. and Singh, L. (2021). The evolution of topic modeling. ACM Comput. Surv.

Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W. e Starnini, M. (2021). The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9):e2023301118.

Cinus, F., Minici, M., Monti, C. e Bonchi, F. (2022). The effect of people recommenders on echo chambers and polarization. Em Proceedings of the International AAAI Conference on Web and Social Media, volume 16, p. 90–101.

Clauset, A., Newman, M. E. e Moore, C. (2004). Finding community structure in very large networks. Physical review E, 70(6):066111.

Cody, E. M., Reagan, A. J., Mitchell, L., Dodds, P. S., and Danforth, C. M. (2015). Climate change sentiment on twitter: An unsolicited public opinion poll. PloS one, 10(8):e0136092.

Coletto, M., Garimella, K., Gionis, A., and Lucchese, C. (2017). Automatic controversy detection in social media: a content-independent motif-based approach. Online Social Networks and Media, 3:22–31.

Colleoni, E., Rozza, A. e Arvidsson, A. (2014). Echo chamber or public sphere? predicting political orientation and measuring political homophily in twitter using big data. Journal of communication, 64(2):317–332.

Conneau, A., Kiela, D., Schwenk, H., Barrault, L., and Bordes, A. (2017). Supervised learning of universal sentence representations from natural language inference data. In Proc of EMNLP, pages 670–680, Copenhagen, Denmark.

Conover, M., Ratkiewicz, J., Francisco, M., Gonçalves, B., Menczer, F. e Flammini, A. (2011). Political polarization on twitter. Em Proceedings of the International AAAI Conference on Web and Social Media, volume 5, p. 89–96.

Conover, M., Ratkiewicz, J., Francisco, M., Gonçalves, B., Menczer, F., and Flammini, A. (2011). Political polarization on twitter. In Proceedings of the international aaai conference on web and social media, volume 5, pages 89–96.

Conover, M., Ratkiewicz, J., Francisco, M., Gonçalves, B., Menczer, F., and Flammini, A. (2011). Political polarization on twitter. In Proceedings of the International AAAI Conference on Web and Social Media, volume 5.

Cossard, A., De Francisci Morales, G., Kalimeri, K., Mejova, Y., Paolotti, D. e Starnini, M. (2020). Falling into the echo chamber: The italian vaccination debate on twitter. Em Proceedings of the International AAAI Conference on Web and Social Media, volume 14, p. 130–140.

COSTA, P. et al. End-to-end adversarial retinal image synthesis. IEEE transactions on medical imaging, IEEE, v. 37, n. 3, p. 781–791, 2017.

Cota, W., Ferreira, S. C., Pastor-Satorras, R. e Starnini, M. (2019). Quantifying echo chamber effects in information spreading over political communication networks. EPJ Data Science, 8(1):1–13. https://doi.org/10.14742/ajet.1344

Cui, P., Wang, X., Pei, J. e Zhu, W. (2019). A survey on network embedding. IEEE Transactions on Knowledge and Data Engineering, 31(5):833–852.

Curiskis, S. A., Drake, B., Osborn, T. R. e Kennedy, P. J. (2020). An evaluation of document clustering and topic modelling in two online social networks: Twitter and reddit. Information Processing & Management, 57(2):102034.

D’Alessio, D. and Allen, M. (2000). Media bias in presidential elections: A meta-analysis. Journal of communication, 50(4):133–156.

DAI, W. et al. Scan: Structure correcting adversarial network for organ segmentation in chest x-rays. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. [S.l.]: Springer, 2018. p. 263–273.

Darwish, K., Magdy, W., and Zanouda, T. (2017). Improved stance prediction in a user similarity feature space. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, ASONAM ’17, page 145–148, New York, NY, USA. Association for Computing Machinery.

Darwish, K., Magdy, W., Rahimi, A., Baldwin, T., and Abokhodair, N. (2018). Predicting online islamophobic behavior after #parisattacks. The Journal of Web Science, 4(3):34–52.

Darwish, K., Stefanov, P., Aupetit, M., and Nakov, P. (2020). Unsupervised user stance detection on twitter. Proceedings of the International AAAI Conference on Web and Social Media, 14(1):141–152.

Dash, A., Mukherjee, A. e Ghosh, S. (2019). A network-centric framework for auditing recommendation systems. Em IEEE INFOCOM 2019 – IEEE Conference on Computer Communications, p. 1990–1998.

de Oliveira, N. R., Medeiros, D. S. e Mattos, D. M. (2020a). A sensitive stylistic approach to identify fake news on social networking. IEEE Signal Processing Letters, 27:1250–1254.

de Oliveira, N. R., Medeiros, D. S. e Mattos, D. M. (2021a). Caracterização sócio-temporal de conteúdos em redes sociais baseada em processamento em fluxo. Em Anais do XXVI Workshop de Gerência e Operação de Redes e Serviços, p. 54–67. SBC.

de Oliveira, N. R., Pisa, P. S., Costa, B., Lopez, M. A., Moraes, I. M. e Mattos, D. M. (2020b). Processamento de linguagem natural para identificação de notícias falsas em redes sociais: Ferramentas, tendências e desafios. Em Minicursos do XX Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais (SBSeg 2020).

de Oliveira, N. R., Pisa, P. S., Lopez, M. A., de Medeiros, D. S. V. e Mattos, D. M. F. (2021b). Identifying fake news on social networks based on natural language processing: Trends and challenges. Information, 12(1).

Deffuant, G., Neau, D., Amblard, F. e Weisbuch, G. (2000). Mixing beliefs among interacting agents. Advances in Complex Systems, 3(01n04):87–98.

Del Vicario, M., Vivaldo, G., Bessi, A., Zollo, F., Scala, A., Caldarelli, G. e Quattrociocchi, W. (2016). Echo chambers: Emotional contagion and group polarization on facebook. Scientific reports, 6(1):1–12.

Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

Dib, F. (2022). Regular expressions 101.

DONAHUE, C.; MCAULEY, J.; PUCKETTE, M. Adversarial audio synthesis. arXiv preprint arXiv:1802.04208, 2018.

DONG HAO-WEN E HSIAO, W.-Y.; YANG, L.-C. e; YANG, Y.-H. Musegan: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment. In: Proceedings of the AAAI Conference on Artificial Intelligence. [S.l.: s.n.], 2018. v. 32, n. 1.

Dong, R., Sun, Y., Wang, L., Gu, Y., and Zhong, Y. (2017). Weakly-guided user stance prediction via joint modeling of content and social interaction. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM ’17, page 1249–1258, New York, NY, USA. Association for Computing Machinery.

Donkers, T. e Ziegler, J. (2021). The dual echo chamber: Modeling social media polarization for interventional recommending. Em Fifteenth ACM Conference on Recommender Systems, RecSys’21, p. 12–22, New York, NY, USA. Association for Computing Machinery. https://doi.org/10.1145/1216016.1216023

Dori-Hacohen, S. and Allan, J. (2015). Automated controversy detection on the web. In European Conference on Information Retrieval, pages 423–434. Springer.

DOUZAL-CHOUAKRIA, A.; AMBLARD, C. Classification trees for time series. Pattern Recognition, Elsevier, v. 45, n. 3, p. 1076–1091, 2012.

DWORK, C. Differential privacy: A survey of results. In: SPRINGER. International conference on theory and applications of models of computation. [S.l.], 2008. p. 1–19.

Easley, D. and Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge university press.

Ebrahimi, J., Dou, D., and Lowd, D. (2016). A joint sentiment-target-stance model for stance classification in tweets. In Proceedings of COLING 2016, the 26th international conference on computational linguistics: Technical papers, pages 2656–2665.

Elfardy, H. and Diab, M. (2016). CU-GWU perspective at SemEval-2016 task 6: Ideological stance detection in informal text. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 434–439, San Diego, California. Association for Computational Linguistics.

ENGEL, J. et al. Gansynth: Adversarial neural audio synthesis. arXiv preprint arXiv:1902.08710, 2019.

ESTEBAN, C.; HYLAND, S. L.; RÄTSCH, G. Real-valued (medical) time series generation with recurrent conditional gans. arXiv preprint arXiv:1706.02633, 2017.

Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4):82–89.

Feng, F., Yang, Y., Cer, D., Arivazhagan, N., and Wang, W. (2020). Language-agnostic bert sentence embedding. arXiv preprint arXiv:2007.01852.

Ferreira, C. H., Murai, F., Silva, A. P., Almeida, J. M., Trevisan, M., Vassio, L., Mellia, M., and Drago, I. (2021). On the dynamics of political discussions on instagram: A network perspective. Online Social Networks and Media, 25:100155.

Ferreira, W. and Vlachos, A. (2016). Emergent: a novel data-set for stance classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1163–1168, San Diego, California. Association for Computational Linguistics.

Ferreira, W. and Vlachos, A. (2019). Incorporating label dependencies in multilabel stance detection. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6350–6354, Hong Kong, China. Association for Computational Linguistics.

Fiorina, M. P., Abrams, S. A., and Pope, J. C. (2008). Polarization in the american public: Misconceptions and misreadings. The Journal of Politics.

Fleiss, J. L., Levin, B., Paik, M. C., et al. (1981). The measurement of interrater agreement. Stat meth rat and prop, 2(212-236).

Fletcher, R., Robertson, C. T. e Nielsen, R. K. (2021). How many people live in politically partisan online news echo chambers in different countries? Journal of Quantitative Description: Digital Media, 1.

FRIGERIO, L. et al. Differentially private generative adversarial networks for time series, continuous, and discrete open data. In: SPRINGER. IFIP International Conference on ICT Systems Security and Privacy Protection. [S.l.], 2019. p. 151–164.

Gamon, M. and Aue, A. (2005). Automatic identification of sentiment vocabulary: Exploiting low association with known sentiment terms. In Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing, pages 57–64, Ann Arbor, Michigan. Association for Computational Linguistics.

GAO, C. et al. Adversarialnas: Adversarial neural architecture search for gans. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. [S.l.: s.n.], 2020. p. 5680–5689.

Garimella, K., De Francisci Morales, G., Gionis, A. e Mathioudakis, M. (2017). Reducing controversy by connecting opposing views. Em Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, p. 81–90.

Garimella, K., De Francisci Morales, G., Gionis, A. e Mathioudakis, M. (2018a). Political discourse on social media: Echo chambers, gatekeepers, and the price of bipartisanship. Em Proceedings of the 2018 World Wide Web Conference - WWW ’18, p. 913–922.

Garimella, K., De Francisci Morales, G., Gionis, A., and Mathioudakis, M. (2017a). Reducing controversy by connecting opposing views. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pages 81–90.Garimella, K. et al. (2018a). Polarization on social media.

Garimella, K., Gionis, A., Parotsidis, N., and Tatti, N. (2017b). Balancing information exposure in social networks. Advances in neural information processing systems, 30.

Garimella, K., Morales, G. D. F., Gionis, A. e Mathioudakis, M. (2018b). Quantifying controversy on social media. Trans. Soc. Comput., 1(1).

Garimella, K., Morales, G. D. F., Gionis, A., and Mathioudakis, M. (2018b). Quantifying controversy on social media. ACM Transactions on Social Computing, 1(1):1–27.

Garimella, K., Smith, T., Weiss, R., and West, R. (2021). Political polarization in online news consumption. In Proceedings of the International AAAI Conference on Web and Social Media, volume 15, pages 152–162.

Garimella, V. R. K. e Weber, I. (2017). A long-term analysis of polarization on twitter. Proceedings of the International AAAI Conference on Web and Social Media, 11(1):528–531.

Gausen, A., Luk, W. e Guo, C. (2022). Using agent-based modelling to evaluate the impact of algorithmic curation on social media. ACM Journal of Data and Information Quality (JDIQ).

Ge, Y., Zhao, S., Zhou, H., Pei, C., Sun, F., Ou, W. e Zhang, Y. (2020). Understanding echo chambers in e-commerce recommender systems. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development inInformation Retrieval, p. 2261–2270.

GHOSH, S. et al. Contextual lstm (clstm) models for large scale nlp tasks. arXiv preprint arXiv:1602.06291, 2016.

Ghosh, S., Singhania, P., Singh, S., Rudra, K., and Ghosh, S. (2019). Stance detection in web and social media: A comparative study. In Crestani, F., Braschler, M., Savoy, J., Rauber, A., Müller, H., Losada, D. E., Heinatz Bürki, G., Cappellato, L., and Ferro, N., editors, Experimental IR Meets Multilinguality, Multimodality, and Interaction, pages 75–87, Cham. Springer International Publishing.

Gillani, N., Yuan, A., Saveski, M., Vosoughi, S. e Roy, D. (2018). Me, my echo chamber, and i: introspection on social media polarization. Em Proceedings of the 2018 World Wide Web Conference, p. 823–831.

Gillani, N., Yuan, A., Saveski, M., Vosoughi, S., and Roy, D. (2018). Me, my echo chamber, and i: Introspection on social media polarization. The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018, pages 823–831.

Gokcekus, S., Firth, J. A., Regan, C., and Sheldon, B. C. (2021). Recognising the key role of individual recognition in social networks. Trends in Ecology & Evolution, 36(11):1024–1035.

González-Bailón, S. and De Domenico, M. (2021). Bots are less central than verified accounts during contentious political events. Proceedings of the National Academy of Sciences, 118(11).

GOODFELLOW, I. et al. Generative adversarial nets. In: Advances in neural information processing systems. [S.l.: s.n.], 2014. p. 2672–2680.

GOODFELLOW, I.; BENGIO, Y.; COURVILLE, A. Deep learning. [S.l.]: MIT press, 2016.

GOOGLE. Web Traffic Timeseries Forecasting. 2018. <https://www.kaggle.com/c/web-traffic-time-series-forecasting>.

Goyal, P. e Ferrara, E. (2018). Graph embedding techniques, applications, and performance: A survey. Knowledge-Based Systems, 151:78–94.

Grover, A. e Leskovec, J. (2016). node2vec: Scalable feature learning for networks. Em Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, p. 855–864.

Grubbs, F. E. (1969). Procedures for detecting outlying observations in samples. Technometrics, 11(1):1–21.

Guerra, P., Meira Jr, W., Cardie, C. e Kleinberg, R. (2013). A measure of polarization on social media networksbased on community boundaries. Em Proceedings of the 7th International Conference on Weblogs and Social Media, ICWSM 2013, p. 215–224.

Guerra, P., Meira Jr, W., Cardie, C., and Kleinberg, R. (2013). A measure of polarization on social media networks based on community boundaries. In Proceedings of the International AAAI Conference on Web and Social Media.

Guimaraes, A. and Weikum, G. (2021). X-posts explained: Analyzing and predicting controversial contributions in thematically diverse reddit forums. In ICWSM, pages 163–172.

Hamidian, S. and Diab, M. T. (2019). Rumor detection and classification for twitter data. CoRR, abs/1912.08926.

Harris, Z. S. (1954). Distributional structure. Word, 10(2-3):146–162.

HARTMANN KAY GREGOR E SCHIRRMEISTER, R. T.; BALL, T. Eeg-gan: Generative adversarial networks for electroencephalograhic (eeg) brain signals. arXiv preprint arXiv:1806.01875, 2018.

HAYKIN, S. Neural Networks and Learning Machines. [S.l.]: Prentice Hall, 2009. ( Neural networks and learning machines, v. 10). ISBN 9780131471399.

Hercig, T., Krejzl, P., Hourová, B., Steinberger, J., and Lenc, L. (2017). Detecting stance in czech news commentaries. ITAT, 176:180.

Hipson, W. E. and Mohammad, S. M. (2021). Emotion dynamics in movie dialogues. PloS one, 16(9):e0256153.

HOCHREITER, S. et al. Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. [S.l.]: A field guide to dynamical recurrent neural networks. IEEE Press In, 2001.

HOCHREITER, S.; SCHMIDHUBER, J. Long short-term memory. Neural computation, MIT Press, v. 9, n. 8, p. 1735–1780, 1997.

Hofmann, T. (2001). Unsupervised learning by probabilistic latent semantic analysis. Machine learning, 42(1):177–196.

Honnibal, M., Montani, I., Van Landeghem, S., and Boyd, A. (2020). spaCy: Industrial-strength Natural Language Processing in Python.

Horawalavithana, S., Ng, K. W., and Iamnitchi, A. (2021). Drivers of polarized discussions on twitter during venezuela political crisis. In 13th ACM Web Science Conference 2021, pages 205–214.

Hosseini, H., Kannan, S., Zhang, B., and Poovendran, R. (2017). Deceiving google’s perspective api built for detecting toxic comments. arXiv preprint arXiv:1702.08138.

HUANG, J.; KURNIAWAN, E.; SUN, S. Cellular kpi anomaly detection with gan and time series decomposition. In: IEEE. ICC 2022-IEEE International Conference on Communications. [S.l.], 2022. p. 4074–4079.

Huh and Fienberg 2012] Huh, S. and Fienberg, S. E. (2012). Discriminative topic modeling based on manifold learning. ACM Transactions on Knowledge Discovery from Data (TKDD), 5(4):1–25.

Indurkhya, N. and Damerau, F. J. (2010). Handbook of natural language processing. Chapman and Hall/CRC.

Iyengar, S., Sood, G., and Lelkes, Y. (2012). Affect, not ideologya social identity perspective on polarization. Public opinion quarterly, 76(3):405–431.

Iyyer, M., Manjunatha, V., Boyd-Graber, J., and Daumé III, H. (2015). Deep unordered composition rivals syntactic methods for text classification. In Proc. of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing, pages 1681–1691.

Jang, M. and Allan, J. (2018). Explaining controversy on social media via stance summarization. 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018, pages 1221–1224.

Jang, M., Foley, J., Dori-Hacohen, S., and Allan, J. (2016). Probabilistic approaches to controversy detection. In Proceedings of the 25th ACM international on conference on information and knowledge management, pages 2069–2072.

JAUHRI, A. et al. Generating realistic ride-hailing datasets using gans. ACM Transactions on Spatial Algorithms and Systems (TSAS), ACM New York, NY, USA, v. 6, n. 3, p. 1–14, 2020.

Jeon, Y., Kim, B., Xiong, A., Lee, D. e Han, K. (2021). Chamberbreaker: Mitigating the echo chamber effect and supporting information hygiene through a gamified inoculation system. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2):1–26.

Jigsaw, G. (2022). Perspective api. Acessed May 31, 2022.

Joulin, A., Grave, E., Bojanowski, P., and Mikolov, T. (2016). Bag of tricks for efficient text classifi cation. arXiv preprint arXiv:1607.01759.

Jurafsky, D. and Martin, J. H. (2021). Speech and language processing 3. Ed., volume 3. Pearson London.

KADRI, F. et al. Towards accurate prediction of patient length of stay at emergency department: a gan-driven deep learning framework. Journal of Ambient Intelligence and Humanized Computing, Springer, p. 1–15, 2022.

KAMPOURAKI, A.; MANIS, G.; NIKOU, C. Heartbeat time series classification with support vector machines. IEEE transactions on information technology in biomedicine, IEEE, v. 13, n. 4, p. 512–518, 2008.

KARIM, F. et al. Multivariate lstm-fcns for time series classification. Neural Networks, Elsevier, v. 116, p. 237–245, 2019.

KARRAS, T. et al. Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196, 2017.

Kawintiranon, K. and Singh, L. (2021). Knowledge enhanced masked language model for stance detection. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4725–4735, Online. Association for Computational Linguistics.

Kemp, S. (2022). Digital 2022: July global statshot report. https://datareportal.com/reports/digital-2022-july-global-statshot. Accessed: 2022-09-08.

Khaleghi, B., Khamis, A., Karray, F. O., and Razavi, S. N. (2013). Multisensor data fusion: A review of the state-of-the-art. Information fusion, 14(1):28–44.

Khder, M. A. (2021). Web scraping or web crawling: State of art, techniques, approaches and application. International Journal of Advances in Soft Computing & Its Applications, 13(3).

Kiros, R., Zhu, Y., Salakhutdinov, R. R., Zemel, R., Urtasun, R., Torralba, A., and Fidler, S. (2015). Skip-thought vectors. Advances in neural information processing systems, 28.

Klayman, J. (1995). Varieties of confirmation bias. Psychology of learning and motivation, 32:385–418.

Kleene, S. C. et al. (1956). Representation of events in nerve nets and fi nite automata. Automata studies, 34:3–41.

Klenner, M., Amsler, M., and Hollenstein, N. (2014). Verb polarity frames: a new resource and its application in target-specific polarity classification.In KONVENS, pages 106–115.

Kobellarz, J. and Silva, T. H. (2022). Should we translate? evaluating toxicity in online comments when translating from portuguese to english. In Simpósio Brasileiro de Sistemas Multimídia e Web (WebMedia), Curitiba, Brasil.

Kobellarz, J. K., Brocic, M., Graeml, A. R., Silver, D., and Silva, T. H. (2022). Reaching the bubble may not be enough: news media role in online political polarization. arXiv.

Kochkina, E., Liakata, M., and Augenstein, I. (2017). Turing at SemEval-2017 task 8: Sequential approach to rumour stance classification with branch-LSTM. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 475–480, Vancouver, Canada. Association for Computational Linguistics.

KOTSIFAKOS, A.; PAPAPETROU, P. Model-based time series classification. In: SPRINGER. International Symposium on Intelligent Data Analysis. [S.l.], 2014. p. 179–191.

Kubin, E. and von Sikorski, C. (2021). The role of (social) media in political polarization: a systematic review. Annals of the International Communication Association, 45(3):188–206.

Kucher, K., Paradis, C., and Kerren, A. (2018). Visual analysis of sentiment and stance in social media texts. In Proceedings of the Eurographics/IEEE VGTC Conference on Visualization: Posters, EuroVis ’18, page 49–51, Goslar, DEU. Eurographics Association.

Küçük, D. and Can, F. (2020). Stance detection: A survey. ACM Comput. Surv., 53(1).

Kulshrestha, J., Eslami, M., Messias, J., Zafar, M. B., Ghosh, S., Gummadi, K. P., and Karahalios, K. (2017). Quantifying search bias: Investigating sources of bias for political searches in social media. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, pages 417–432.

Kumar, S., Hamilton, W. L., Leskovec, J., and Jurafsky, D. (2018). Community interaction and conflict on the web. In Proc of WWW, WWW ’18, page 933–943, Republic and Canton of Geneva, CHE.

Ladeira, L. Z., Santos, F., Cléopas, L., Buteneers, P., and Villas, L. (2022). Neonda: Neo natural language data augmentation. In 2022 IEEE 16th International Conference on Semantic Computing (ICSC), pages 99–102. IEEE.

Lafferty, J., McCallum, A., and Pereira, F. C. (2001). Conditional random fi elds: Probabilistic models for segmenting and labeling sequence data. ACM International Conference on Machine Learning, pages 282–289.

Lai, M., Farías, D. I. H., Patti, V., and Rosso, P. (2017). Friends and enemies of clinton and trump: Using context for detecting stance in political tweets. CoRR, abs/1702.08021.Lelkes, Y. (2016). Mass polarization: Manifestations and measurements. Public Opinion Quarterly, 80(S1):392–410.

Lal, S., Tiwari, L., Ranjan, R., Verma, A., Sardana, N., and Mourya, R. (2020). Analysis and classification of crime tweets. Procedia Computer Science, 167:1911–1919. International Conference on Computational Intelligence and Data Science.

Landauer, T. K., Foltz, P. W., and Laham, D. (1998). An introduction to latent semantic analysis. Discourse processes, 25(2-3):259–284.

Li, C., Porco, A., and Goldwasser, D. (2018). Structured representation learning for online debate stance prediction. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3728–3739, Santa Fe, New Mexico, USA. Association for Computational Linguistics.

Li, Y. and Caragea, C. (2019). Multi-task stance detection with sentiment and stance lexicons. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6299–6305, Hong Kong, China. Association for Computational Linguistics.

LIM, B.; ZOHREN, S. Time-series forecasting with deep learning: a survey. Philosophical Transactions of the Royal Society A, The Royal Society Publishing, v. 379, n. 2194, p. 20200209, 2021.

Lima, L., Reis, J. C., Melo, P., Murai, F., Araujo, L., Vikatos, P., and Benevenuto, F. (2018). Inside the right-leaning echo chambers: Characterizing gab, an unmoderated social system. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 515–522. IEEE.

LIN, Z. et al. Using gans for sharing networked time series data: Challenges, initial promise, and open questions. In: Proceedings of the ACM Internet Measurement Conference. [S.l.: s.n.], 2020. p. 464–483.

Liu, B. (2010). Sentiment analysis and subjectivity. In Handbook of Natural Language Processing, Second Edition. Taylor and Francis Group, Boca.

Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1):1–167.

Liu, C., Li, W., Demarest, B., Chen, Y., Couture, S., Dakota, D., Haduong, N., Kaufman, N., Lamont, A., Pancholi, M., Steimel, K., and Kübler, S. (2016). IUCL at SemEval2016 task 6: An ensemble model for stance detection in Twitter. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 394–400, San Diego, California. Association for Computational Linguistics.

Liu, R. e Krishnan, A. (2021). Pecanpy: a fast, efficient and parallelized python implementation of node2vec. Bioinformatics, 37(19):3377–3379.

Lu, H., Caverlee, J., and Niu, W. (2015). Biaswatch: A lightweight system for discovering and tracking topic-sensitive opinion bias in social media. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pages 213–222.

LUO, Y. et al. Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems, v. 31, 2018.

MacQueen, J. (1967). Classification and analysis of multivariate observations. In 5th Berkeley Symp. Math. Statist. Probability, pages 281–297.

MAHARAJ, E. A.; ALONSO, A. M. Discriminant analysis of multivariate time series: Application to diagnosis based on ecg signals. Computational Statistics & Data Analysis, Elsevier, v. 70, p. 67–87, 2014.

Malandrino, F.; Chiasserini, C.; Kirkpatrick, S. Cellular network traces towards 5g: Usage, analysis and generation. IEEE Transactions on Mobile Computing, v. 17, n. 3, p. 529–542, 2018.

Matakos, A., Terzi, E., and Tsaparas, P. (2017). Measuring and moderating opinion polarization in social networks. Data Mining and Knowledge Discovery, 31(5):1480–1505.

McPherson, M., Smith-Lovin, L., and Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual review of sociology, pages 415–444.

Medhat, W., Hassan, A., and Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal, 5(4):1093–1113.

Mejova, Y., Zhang, A. X., Diakopoulos, N., and Castillo, C. (2014). Controversy and sentiment in online news. arXiv preprint arXiv:1409.8152.

Mesnil, G., Dauphin, Y., Yao, K., Bengio, Y., Deng, L., Hakkani-Tur, D., He, X., Heck, L., Tur, G., Yu, D., et al. (2014). Using recurrent neural networks for slot filling in spoken language understanding. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(3):530–539.

Messias, J., Schmidt, L., Oliveira, R., and Benevenuto, F. (2013). You followed my bot! transforming robots into influential users in twitter. First Monday.

Meyer-Baese, A. e Schmid, V. (2014). Chapter 2 – feature selection and extraction. Em Meyer-Baese, A. e Schmid, V., editors, Pattern Recognition and Signal Analysis in Medical Imaging (Second Edition), p. 21–69. Academic Press, Oxford, second edition edição.

Mikolov, T., Chen, K., Corrado, G. e Dean, J. (2013). Efficient estimation of word representations in vector space. Em 1st International Conference on Learning Representations, ICLR, p. 1–12.

Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Effi cient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.

Milroy, L. and Llamas, C. (2013). Social networks. The handbook of language variation and change, pages 407–427.

Minici, M., Cinus, F., Monti, C., Bonchi, F. e Manco, G. (2022). Cascade-based echo chamber detection. arXiv preprint arXiv:2208.04620. https://doi.org/10.1145/2072609.2072625

Mitchell, J. C. (1974). Social networks. Annual review of anthropology, 3:279–299.

MOGREN, O. C-rnn-gan: Continuous recurrent neural networks with adversarial training. arXiv preprint arXiv:1611.09904, 2016.

Mohammad, S., Kiritchenko, S., Sobhani, P., Zhu, X., and Cherry, C. (2016). SemEval2016 task 6: Detecting stance in tweets. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 31–41, San Diego, California. Association for Computational Linguistics.

Mohammad, S. M. and Turney, P. D. (2013). Crowdsourcing a word-emotion association lexicon. Computational Intelligence, 29(3):436–465.

Mohammad, S. M., Sobhani, P., and Kiritchenko, S. (2017). Stance and sentiment in tweets. ACM Trans. Internet Technol., 17(3).

MONTGOMERY, D. C.; HINES, W. W. Probability and statistics in engineering and management science. [S.l.]: John Wiley & Sons, 1980.

Morales, A. J., Borondo, J., Losada, J. C., and Benito, R. M. (2015). Measuring political polarization: Twitter shows the two sides of venezuela. Chaos: An Interdisciplinary Journal of Nonlinear Science, 25(3):033114.

Morales, G. D. F., Monti, C. e Starnini, M. (2021). No echo in the chambers of political interactions on reddit. Scientific reports, 11(1):1–12. https://doi.org/10.1145/1526709.1526754

Moreira, R. C., Vaz-de Melo, P. O., and Pappa, G. L. (2020). Elite versus mass polarization on the brazilian impeachment proceedings of 2016. Social Network Analysis and Mining.

Morini, V., Pollacci, L. e Rossetti, G. (2021). Toward a standard approach for echo chamber detection: Reddit case study. Applied Sciences, 11(12).

Munson, S., Lee, S. e Resnick, P. (2013). Encouraging reading of diverse political viewpoints with a browser widget. Em Proceedings of The International AAAI Conference on Web and Social Media, volume 7, p. 419–428.

Murtagh and Contreras 2012] Murtagh, F. and Contreras, P. (2012). Algorithms for hierarchical clustering: an overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(1):86–97.

Mutlu, E. C., Oghaz, T., Jasser, J., Tutunculer, E., Rajabi, A., Tayebi, A., Ozmen, O., and Garibay, I. (2020). A stance data set on polarized conversations on twitter about the efficacy of hydroxychloroquine as a treatment for covid-19. Data in brief, 33:106401.

Newman, M. E. (2006). Finding community structure in networks using the eigenvectors of matrices. Physical review E, 74(3):036104.

Nguyen, C. T. (2020). Echo chambers and epistemic bubbles. Episteme, 17(2):141–161.

OH, E. et al. Sting: Self-attention based time-series imputation networks using gan. In: IEEE. 2021 IEEE International Conference on Data Mining (ICDM). [S.l.], 2021. p. 1264–1269.

Oliveira, W. B. d., Dorini, L. B., Minetto, R., and Silva, T. H. (2020). Outdoorsent: Sentiment analysis of urban outdoor images by using semantic and deep features. ACM Trans. Inf. Syst., 38(3).

Omran, M. G., Engelbrecht, A. P., and Salman, A. (2007). An overview of clustering methods. Intelligent Data Analysis, 11(6):583–605.

Ou, M., Cui, P., Pei, J., Zhang, Z. e Zhu, W. (2016). Asymmetric transitivity preserving graph embedding. Em Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p. 1105–1114. https://doi.org/10.1145/2468356.2468384

Page, L., Brin, S., Motwani, R. e Winograd, T. (1999). The pagerank citation ranking: Bringing order to the web. Relatório técnico, Stanford InfoLab.

Palla, G., Derényi, I., Farkas, I., and Vicsek, T. (2005). Uncovering the overlapping community structure of complex networks in nature and society. nature, 435(7043):814.

Pang, B., Lee, L., et al. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in information retrieval, 2(1–2):1–135.

Pannucci, C. J. and Wilkins, E. G. (2010). Identifying and avoiding bias in research. Plastic and reconstructive surgery, 126(2):619.

Pariser, E. (2011). The Filter Bubble: What The Internet Is Hiding From You. Penguin Books Limited.

Pennebaker, J. W., Francis, M. E., and Booth, R. J. (2001). Linguistic inquiry and word count: Liwc 2001. Mahway: Lawrence Erlbaum Associates, 71(2001):2001.

Pennington, J., Socher, R., and Manning, C. (2014). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP).

Pergola, G., Gui, L., and He, Y. (2020). A disentangled adversarial neural topic model for separating opinions from plots in user reviews. arXiv preprint arXiv:2010.11384.

Perozzi, B., Al-Rfou, R. e Skiena, S. (2014). Deepwalk: Online learning of social representations. Em Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, p. 701–710.

Phillips, D. (2017). How directions on the waze app led to death in brazil’s favelas. https://goo.gl/QxHdKv. Accessed: 2022-08-29.

Plutchik, R. (1980). A general psychoevolutionary theory of emotion. In Theories of emotion, pages 3–33. Elsevier.

Pombo, O. (2022). Morte de sócrates.

Pons, P. e Latapy, M. (2005). Computing communities in large networks using random walks. Em International symposium on computer and information sciences, p. 284–293. Springer.

Popescu, A.-M. and Pennacchiotti, M. (2010). Detecting controversial events from twitter. In Proceedings of the 19th ACM international conference on Information and knowledge management, pages 1873–1876.

Porter, M. F. (1980). An algorithm for suffix stripping. Program.

POVINELLI, R. J. et al. Time series classification using gaussian mixture models of reconstructed phase spaces. IEEE Transactions on Knowledge and Data Engineering, IEEE, v. 16, n. 6, p. 779–783, 2004.

Proferes, N., Jones, N., Gilbert, S., Fiesler, C., and Zimmer, M. (2021). Studying reddit: A systematic overview of disciplines, approaches, methods, and ethics. Social Media+ Society, 7(2):20563051211019004.

QU, Y. et al. Gan-driven personalized spatial-temporal private data sharing in cyber-physical social systems. IEEE Transactions on Network Science and Engineering, IEEE, 2020.

Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al. (2018). Improving language understanding by generative pre-training. OpenAI Blog.

Rajendran, G., Chitturi, B., and Poornachandran, P. (2018). Stance-in-depth deep neural approach to stance classification. Procedia Computer Science, 132:1646–1653. International Conference on Computational Intelligence and Data Science.

Ramage, D., Rosen, E., Chuang, J., Manning, C. D., and McFarland, D. A. (2009). Topic modeling for the social sciences. In NIPS 2009 workshop on applications for topic models: text and beyond, volume 5, pages 1–4.

RAMOS, H. S. et al. Aprendizado federado aplicado à internet das coisas. Sociedade Brasileira de Computação, 2021.

RAO, J. et al. Lstm-trajgan: A deep learning approach to trajectory privacy protection. arXiv preprint arXiv:2006.10521, 2020.

Rashed, A., Kutlu, M., Darwish, K., Elsayed, T., and Bayrak, C. (2020). Embeddingsbased clustering for target specific stances: The case of a polarized turkey. CoRR, abs/2005.09649.

Rathje, S., Van Bavel, J. J., and Van Der Linden, S. (2021). Out-group animosity drives engagement on social media. Proceedings of the National Academy of Sciences, 118(26):e2024292118.

Ravi, K. and Ravi, V. (2015). A survey on opinion mining and sentiment analysis. Know.Based Syst., 89(C):14–46.

Rehurek, R. and Sojka, P. (2010). Software Framework for Topic Modelling with Large Corpora. In Proc. of LREC, Workshop, pages 45–50, Valletta, Malta. ELRA.

Reimers, N. and Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084.

Reimers, N. and Gurevych, I. (2020). Making monolingual sentence embeddings multilingual using knowledge distillation. arXiv preprint arXiv:2004.09813.

Rendel, A., Fernandez, R., Hoory, R., and Ramabhadran, B. (2016). Using continuous lexical embeddings to improve symbolic-prosody prediction in a text-to-speech front-end. In Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. IEEE.

Rettore, P. H., Santos, B. P., Campolina, A. B., Villas, L. A., and Loureiro, A. A. (2016). Towards intra-vehicular sensor data fusion. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pages 126–131. IEEE.

Ribeiro, F. N., Saha, K., Babaei, M., Henrique, L., Messias, J., Benevenuto, F., Goga, O., Gummadi, K. P., and Redmiles, E. M. (2019). On microtargeting socially divisive ads: A case study of russia-linked ad campaigns on facebook. In Proceedings of the Conference on Fairness, Accountability, and Transparency, pages 140–149.

RIBEIRO, I. et al. Mobility and community detection based on topics of interest. In: 2021 IEEE 18th Annual Consumer Communications Networking Conference (CCNC). [S.l.]: IEEE, 2021. p. 1 – 6.

RIBEIRO, I. et al. Uma abordagem para geração de séries temporais de mobilidade urbana baseada em aprendizado profundo. In: Anais do V Workshop de Computação Urbana. Porto Alegre, RS, Brasil: SBC, 2021. p. 251–264. ISSN 2595-2706.

Robertson, S. (2004). Understanding inverse document frequency: on theoretical arguments for idf. Journal of documentation.

Röder, M., Both, A., and Hinneburg, A. (2015). Exploring the space of topic coherence measures. In Proceedings of the eighth ACM international conference on Web search and data mining, pages 399–408.

Rodrigues, N. (1997). Flor de obsessão: as 1000 melhores frases de Nelson Rodrigues, volume 12. Companhiadas Letras.

Rogers, R. (2009). The end of the virtual: Digital methods, volume 339. Amsterdam University Press.

Rossetti, G. e Cazabet, R. (2018). Community discovery in dynamic networks: A survey. ACM Comput. Surv., 51(2).

Rosvall, M., Axelsson, D. e Bergstrom, C. T. (2009). The map equation. The European Physical Journal Special Topics, 178(1):13–23.

Roweis, S. T. e Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. science, 290(5500):2323–2326.

Roy, S. and Goldwasser, D. (2020). Weakly supervised learning of nuanced frames for analyzing polarization in news media. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7698–7716, Online. Association for Computational Linguistics.

RUMELHART, D. E.; HINTON, G. E.; WILLIAMS, R. J. Learning internal representations by error propagation. [S.l.], 1985.

RUMELHART, D. E.; HINTON, G. E.; WILLIAMS, R. J. Learning representations by back-propagating errors. nature, Nature Publishing Group, v. 323, n. 6088, p. 533–536, 1986.

Sailunaz, K., Dhaliwal, M., Rokne, J., and Alhajj, R. (2018). Emotion detection from text and speech - a survey. Social Network Analysis and Mining (SNAM), Springer, 8.

SALIMANS, T. et al. Improved techniques for training gans. Advances in neural information processing systems, v. 29, p. 2234–2242, 2016.

Santos, B. P., Silva, L. A., Celes, C. S., Borges Neto, J. B., Peres, B. S., Vieira, M. A. M., Vieira, L. F. M., Goussevskaia, O. N., and Loureiro, A. A. (2016). Internet das coisas: da teoria à prática. Minicursos SBRC-Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuıdos.

Santos, F. A., Silva, T. H., Loureiro, A. A., and Villas, L. A. (2020a). Automatic extraction of urban outdoor perception from geolocated free texts. Social Network Analysis and Mining, 10(1):1–23.

Santos, G., Mota, V. F. S., Benevenuto, F., and Silva, T. H. (2020b). Neutrality may matter: sentiment analysis in reviews of Airbnb, Booking, and Couchsurfing in Brazil and USA. Social Network Analysis and Mining, 10(1):45.

Sasahara, K., Chen, W., Peng, H., Ciampaglia, G. L., Flammini, A. e Menczer, F. (2021). Social influence and unfollowing accelerate the emergence of echo chambers. Journal of Computational Social Science, 4(1):381–402.

Schütze, H., Manning, C. D., and Raghavan, P. (2008). Introduction to information retrieval, volume 39. Cambridge University Press Cambridge.

SCOTT, J. et al. CRAWDAD dataset cambridge/haggle (v. 2009-05-29). 2009. Downloaded from https://crawdad.org/cambridge/haggle/20090529.

Sculley, D. (2010). Web-scale k-means clustering. In Proceedings of the 19th international conference on World wide web, pages 1177–1178.

Sen, I., Flöck, F., and Wagner, C. (2020). On the reliability and validity of detecting approval of political actors in tweets. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1413–1426, Online. Association for Computational Linguistics.

Shahrezaye, M., Papakyriakopoulos, O., Serrano, J. C. M., and Hegelich, S. (2019). Measuring the ease of communication in bipartite social endorsement networks: A proxy to study the dynamics of political polarization. ACM International Conference Proceeding Series, pages 158–165.

SHUMWAY, R. H.; STOFFER, D. S. Time series analysis and its applications. [S.l.]: Springer, 2000. v. 3.

Siddiqua, U. A., Chy, A. N., and Aono, M. (2019). Tweet stance detection using an attention based neural ensemble model. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1868–1873, Minneapolis, Minnesota. Association for Computational Linguistics.

Silva, T. H., Viana, A. C., Benevenuto, F., Villas, L., Salles, J., Loureiro, A., and Quercia, D. (2019). Urban computing leveraging location-based social network data: A survey. ACM Comput. Surv., 52(1):17:1–17:39.

SINGH, H.; RAY, M. R. Synthetic stream flow generation of river gomti using arima model. In: Advances in Civil Engineering and Infrastructural Development. [S.l.]: Springer, 2021. p. 255–263.

SINGH, N. K.; RAZA, K. Medical image generation using generative adversarial networks: A review. Health informatics: A computational perspective in healthcare, Springer, p. 77–96, 2021.

Sîrbu, A., Pedreschi, D., Giannotti, F. e Kertész, J. (2019). Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model. PloS one, 14(3):e0213246.

Sivakumar, S., Videla, L. S., Kumar, T. R., Nagaraj, J., Itnal, S., and Haritha, D. (2020). Review on word2vec word embedding neural net. In Proc of ICOSEC, pages 282–290. IEEE.

Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A. Y., and Potts, C. (2013). Recursive deep models for semantic compositionality over a sentiment treebank. In Proc of EMNLP, pages 1631–1642.

Sparck Jones, K. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of documentation.

Srivastava, S., Khurana, P., and Tewari, V. (2018). Identifying aggression and toxicity in comments using capsule network. In Proc of TRAC, pages 98–105, Santa Fe, New Mexico, USA.

Stats, I. L. (2022). Twitter usage statistics. https://www.internetlivestats.com/twitter-statistics/. Accessed: 2022-09-08.

Stone, P. J., Dunphy, D. C., and Smith, M. S. (1966). The general inquirer: A computer approach to content analysis. Journal of Regional Science.

Sunstein, C. R. (1999). The law of group polarization. University of Chicago Law School, John M. Olin Law & Economics Working Paper.

Sunstein, C. R. (2018). # Republic: Divided democracy in the age of social media. Princeton University Press.

SUSSKIND, J.; ANDERSON, A.; HINTON, G. E. The Toronto face dataset. [S.l.], 2010.

Swami, S., Khandelwal, A., Singh, V., Akhtar, S. S., and Shrivastava, M. (2018). An english-hindi code-mixed corpus: Stance annotation and baseline system. CoRR, abs/1805.11868.

SYKACEK, P.; ROBERTS, S. J. Bayesian time series classification. Advances in Neural Information Processing Systems, v. 14, 2001.

Tachaiya, J., Irani, A., Esterling, K. M., and Faloutsos, M. (2021). Sentistance: Quantifying the intertwined changes of sentiment and stance in response to an event in online forums. In Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM ’21, page 361–368, New York, NY, USA. Association for Computing Machinery.

Tan, P.-N., Steinbach, M., and Kumar, V. (2018). Introduction to data mining. Pearson Education, 2nd edition.

Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J. e Mei, Q. (2015). Line: Large-scale information network embedding. Em Proceedings of the 24th international conference on world wide web, p. 1067–1077.

Tausczik, Y. R. and Pennebaker, J. W. (2010). The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology, 29(1):24–54.

Terragni, S., Fersini, E., Galuzzi, B. G., Tropeano, P., and Candelieri, A. (2021). OCTIS: Comparing and optimizing topic models is simple! In Proc. of the 16th Conference of the European Chapter of the ACL, pages 263–270.

Terren, L. e Borge-Bravo, R. (2021). Echo chambers on social media: a systematic review of the literature. Review of Communication Research, 9:99–118.

Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., and Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American society for information science and technology, 61(12):2544–2558.

Thomas, M. and Joy, A. T. (2006). Elements of information theory. Wiley-Interscience.

Thompson, K. (1968). Programming techniques: Regular expression search algorithm. Communications of the ACM, 11(6):419–422.

Tokita, C. K., Guess, A. M. e Tarnita, C. E. (2021). Polarized information ecosystems can reorganize social networks via information cascades. Proceedings of the National Academy of Sciences, 118(50):e2102147118.

Tokita, C. K., Guess, A. M., and Tarnita, C. E. (2021). Polarized information ecosystems can reorganize social networks via information cascades. Proceedings of the National Academy of Sciences of the United States of America, 118(50).

Törnberg, P. (2018). Echo chambers and viral misinformation: Modeling fake news as complex contagion. PLoS one, 13(9):e0203958.

Tsakalidis, A., Aletras, N., Cristea, A. I., and Liakata, M. (2018). Nowcasting the stance of social media users in a sudden vote: The case of the greek referendum. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM ’18, page 367–376, New York, NY, USA. Association for Computing Machinery.

Tsytsarau, M., Palpanas, T., and Denecke, K. (2011). Scalable detection of sentimentbased contradictions. DiversiWeb, WWW, 1:9–16.

Tucker, J. A., Guess, A., Barberá, P., Vaccari, C., Siegel, A., Sanovich, S., Stukal, D., and Nyhan, B. (2018). Social media, political polarization, and political disinformation: A review of the scientific literature. Political polarization, and political disinformation: a review of the scientific literature (March 19, 2018).

Tufekci, Z. (2014). Big questions for social media big data: Representativeness, validity and other methodological pitfalls. In Eighth international AAAI conference on weblogs and social media.

Ushioda, A. (1996). Hierarchical clustering of words and application to nlp tasks. In Fourth Workshop on Very Large Corpora. https://doi.org/10.1145/289444.289509

Valensise, C. M., Cinelli, M., and Quattrociocchi, W. (2022). The dynamics of online polarization. arXiv preprint arXiv:2205.15958.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.

Vayansky, I. and Kumar, S. A. (2020). A review of topic modeling methods. Information Systems, 94:101582.

Vicario, M. D., Quattrociocchi, W., Scala, A., and Zollo, F. (2019). Polarization and fake news: Early warning of potential misinformation targets. ACM Transactions on the Web (TWEB), 13(2):1–22.

Villa, G., Pasi, G. e Viviani, M. (2021). Echo chamber detection and analysis. Social Network Analysis and Mining, 11(1):1–17.

von Nordheim, G., Boczek, K., and Koppers, L. (2018). Sourcing the sources. Digital Journalism, 6(7):807–828.

Waller, I. and Anderson, A. (2021). Quantifying social organization and political polarization in online platforms. Nature, 600(7888):264–268.

Wandelt, S., Shi, X. e Sun, X. (2020). Complex network metrics: Can deep learning keep up with tailor-made reference algorithms? IEEE Access, 8:68114–68123.

Wang, D., Cui, P. e Zhu, W. (2016). Structural deep network embedding. Em Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, p. 1225–1234.

Wang, X. and McCallum, A. (2006). Topics over time: a non-markov continuous-time model of topical trends. In Proc. of the 12th ACM SIGKDD, pages 424–433.

WANG, Z. et al. Data-augmentation-based cellular traffic prediction in edge-computing-enabled smart city. IEEE Transactions on Industrial Informatics, IEEE, v. 17, n. 6, p. 4179–4187, 2020.

Watts, D. J., Rothschild, D. M., and Mobius, M. (2021). Measuring the news and its impact on democracy. Proceedings of the National Academy of Sciences, 118(15).

Weedon, J., Nuland, W., and Stamos, A. (2017). Information operations and facebook. Retrieved from Facebook: [link].

WEI, L.; KEOGH, E. Semi-supervised time series classification. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. [S.l.: s.n.], 2006. p. 748–753.

Weld, G., Glenski, M., and Althoff, T. (2021). Political bias and factualness in news sharing across more than 100,000 online communities. arXiv preprint arXiv:2102.08537.

Welling, M. e Kipf, T. N. (2016). Semi-supervised classification with graph convolutional networks. Em J. International Conference on Learning Representations (ICLR 2017).

Wen, Z., Li, Y., and Tao, J. (2016). The parameterized phoneme identity feature as a continuous real-valued vector for neural network based speech synthesis. In INTERSPEECH.

Williams, H. T., McMurray, J. R., Kurz, T. e Lambert, F. H. (2015). Network analysis reveals open forums and echo chambers in social media discussions of climate change. Global environmental change, 32:126–138.

Woodrum, E. and Davison, B. L. (1992). Reexamination of religious influences on abortion attitudes. Review of religious research, pages 229–243.

WUNSCH, D.; XU, R. Clustering. [S.l.]: John Wiley & Sons, 2008.

Xu, M. (2021). Understanding graph embedding methods and their applications. SIAM Review, 63(4):825–853.

Yan, P. (2019). Information bridges: Understanding the informational role of network brokerages in polarised online discourses. In Proc. of ICIS, pages 377–388. Springer.

Yang, M., Wen, X., Lin, Y.-R., and Deng, L. (2017). Quantifying content polarization on twitter. In 2017 IEEE 3rd international conference on collaboration and internet computing (CIC), pages 299–308. IEEE.

Yang, X., Chen, Y.-N., Hakkani-Tür, D., Crook, P., Li, X., Gao, J., and Deng, L. (2017). End-to-end joint learning of natural language understanding and dialogue manager. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5690–5694. IEEE.

Yang, Y., Cer, D., Ahmad, A., Guo, M., Law, J., Constant, N., Abrego, G. H., Yuan, S., Tar, C., Sung, Y.-H., et al. (2019). Multilingual universal sentence encoder for semantic retrieval. arXiv preprint arXiv:1907.04307.

Yang, Z., Algesheimer, R. e Tessone, C. J. (2016). A comparative analysis of community detection algorithms on artificial networks. Scientific reports, 6(1):1–18.

YI, X.; WALIA, E.; BABYN, P. Generative adversarial network in medical imaging: A review. Medical image analysis, Elsevier, v. 58, p. 101552, 2019.

YI, X.; WALIA, E.; BABYN, P. Unsupervised and semi-supervised learning with categorical generative adversarial networks assisted by wasserstein distance for dermoscopy image classification. arXiv preprint arXiv:1804.03700, 2018.

YOON, J.; JARRETT, D.; SCHAAR, M. van der. Time-series generative adversarial networks. In: WALLACH, H. et al. (Ed.). Advances in Neural Information Processing Systems. Curran Associates, Inc., 2019. v. 32. Disponível em: [link].

YU, L. et al. Seqgan: Sequence generative adversarial nets with policy gradient. In: Proceedings of the AAAI conference on artificial intelligence. [S.l.: s.n.], 2017. v. 31, n. 1.

Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Lin, S. M., Zhang, W., Zhang, P. e Sun, H. (2020). Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics, 36(4):1241–1251.

ZAKI, M. J.; JR, W. M. Data mining and machine learning: Fundamental concepts and algorithms. [S.l.]: Cambridge University Press, 2020.

Zannettou, S., Bradlyn, B., De Cristofaro, E., Kwak, H., Sirivianos, M., Stringini, G. e Blackburn, J. (2018). What is gab: A bastion of free speech or an alt-right echo chamber. Em Companion Proceedings of the The Web Conference 2018, p. 1007–1014.

ZHANG, A. et al. Dive into deep learning. arXiv preprint arXiv:2106.11342, 2021.

Zhang, A., Lipton, Z. C., Li, M., and Smola, A. J. (2021). Dive into deep learning. arXiv preprint arXiv:2106.11342.

Zhang, B., Yang, M., Li, X., Ye, Y., Xu, X., and Dai, K. (2020). Enhancing cross-target stance detection with transferable semantic-emotion knowledge. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3188–3197, Online. Association for Computational Linguistics.

ZHANG, C. et al. Generative adversarial network for synthetic time series data generation in smart grids. In: IEEE. 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). [S.l.], 2018. p. 1–6.

ZHANG, D.; MA, M.; XIA, L. A comprehensive review on gans for time-series signals. Neural Computing and Applications, Springer, p. 1–21, 2022.

ZHANG, G. P. Time series forecasting using a hybrid arima and neural network model. Neurocomputing, Elsevier, v. 50, p. 159–175, 2003.

ZHANG, L. Stggan: Spatial-temporal graph generation. In: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. [S.l.: s.n.], 2019. p. 608–609.

Zhang, L., Wang, S., and Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4):e1253.

ZHANG, Y. et al. Gcgan: Generative adversarial nets with graph cnn for network-scale traffic prediction. In: IEEE. 2019 International Joint Conference on Neural Networks (IJCNN). [S.l.], 2019. p. 1–8.

ZHANG, Y. et al. Trafficgan: Network-scale deep traffic prediction with generative adversarial nets. IEEE Transactions on Intelligent Transportation Systems, IEEE, v. 22, n. 1, p. 219–230, 2019.

Zhao, L. and Feng, Z. (2018). Improving slot filling in spoken language understanding with joint pointer and attention. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 426–431.

Zhao, Y. and Karypis, G. (2002). Evaluation of hierarchical clustering algorithms for document datasets. In Proceedings of the eleventh international conference on Information and knowledge management, pages 515–524.

ZHENG, Y. et al. Mining interesting locations and travel sequences from gps trajectories. In: ACM. World wide web. [S.l.], 2009. p. 791–800.

Zhu, X. and Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report.

Zimmer, M. (2020). “but the data is already public”: on the ethics of research in facebook. In The Ethics of Information Technologies, pages 229–241. Routledge.

Zipf, G. K. (2016). Human behavior and the principle of least effort: An introduction to human ecology. Ravenio Books.

Zollo, F., Bessi, A., Del Vicario, M., Scala, A., Caldarelli, G., Shekhtman, L., Havlin, S. e Quattrociocchi, W. (2017). Debunking in a world of tribes. PloS one, 12(7):e0181821.