Mapeamento Sistemático da Literatura sobre a Caracterização do Usuário do Twitter

  • João Marcelo Silva de Oliveira UFPA
  • Isadora Mendes dos Santos UFPA
  • Marcelle Pereira Mota UFPA

Resumo


As redes sociais possuem um vasto conjunto de dados dos seus usuários. Coletar estes dados, transformá-los em informação e, posteriormente, em conhecimento, tem importância ímpar, não apenas para as empresas proprietárias destas redes mas para todo o “ecossistema”nestas redes. Este artigo apresenta um mapeamento sistemático da literatura e teve por objetivo encontrar uma resposta para o seguinte questionamento: quem é o usuário do twitter? Os artigos foram coletados das bases ACM Digital Library, Science Direct e IEEE Xplorer, conforme string definida, utilizando o método de busca automática. Dos artigos selecionados, foram retiradas 8 categorias de identificação de usuários: indivíduo ou organização, multirredes, malicioso, saúde, comportamento, demografia, interesses e identidade. Também a literatura cinzenta foi consultada para integrar o resultado a respeito do usuário do Twitter e gerou informações como a quantidade de usuários por gênero e os países com mais usuários do Twitter.

Referências

Abbasi, R., Rehman, G., Lee, J., Riaz, F. M., and Luo, B. (2017). Discovering temporal user interest on twitter by using semantic based dynamic interest finding model (tut). In 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pages 743–747.

Abrol, S., Khan, L., and Thuraisingham, B. (2012a). Tweecalization: Efficient and intelligent location mining in twitter using semi-supervised learning. In 8th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom), pages 514–523.

Abrol, S., Khan, L., and Thuraisingham, B. (2012b). Tweeque: Spatio-temporal analysis of social networks for location mining using graph partitioning. In 2012 International Conference on Social Informatics, pages 145–148.

Agarwal, S. and Mehta, S. (2020). Effective influence estimation in twitter using temporal, profile, structural and interaction characteristics. Information Processing Management, 57(6):102321.

Ahmad, N. and Siddique, J. (2017). Personality assessment using twitter tweets. Procedia Computer Science, 112:1964–1973. Knowledge-Based and Intelligent Information Engineering Systems: Proceedings of the 21st International Conference, KES-20176-8 September 2017, Marseille, France.

Ahmad, W. and Ali, R. (2018). Understanding the users personal attributes selection tendency across social networks. In 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU), pages 1–6.

Al Maruf, H., Meshkat, N., Ali, M. E., and Mahmud, J. (2015). Human behaviour in different social medias: A case study of twitter and disqus. In 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 270–273.

Al-Qurishi, M., Hossain, M. S., Alrubaian, M., Rahman, S. M. M., and Alamri, A. (2018). Leveraging analysis of user behavior to identify malicious activities in large-scale social networks. IEEE Transactions on Industrial Informatics, 14(2):799–813.

Al-Zoubi, A. M., Faris, H., Alqatawna, J., and Hassonah, M. A. (2018). Evolving support vector machines using whale optimization algorithm for spam profiles detection on online social networks in different lingual contexts. Knowledge-Based Systems, 153:91–104.

Alami, S. and Elbeqqali, O. (2015). Cybercrime profiling: Text mining techniques to detect and predict criminal activities in microblog posts. In 2015 10th International Conference on Intelligent Systems: Theories and Applications (SITA), pages 1–5.

Alharthi, R., Alhothali, A., and Moria, K. (2019). Detecting and characterizing arab spammers campaigns in twitter. Procedia Computer Science, 163:248–256. 16th Learning and Technology Conference 2019Artificial Intelligence and Machine Learning: Embedding the Intelligence.

Alp, Z. Z. and Öğüdücü, c. G. (2016). Influential user detection on twitter: Analyzing effect of focus rate. In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM ’16, page 1321–1328. IEEE Press.

Alvari, H., Beigi, G., Sarkar, S., Ruston, S. W., Corman, S. R., Davulcu, H., and Shakarian, P. (2020). A feature-driven approach for identifying pathogenic social media accounts. In 2020 3rd International Conference on Data Intelligence and Security (ICDIS), pages 26–33.

Alvari, H., Sarkar, S., and Shakarian, P. (2019a). Detection of violent extremists in social media. In 2019 2nd International Conference on Data Intelligence and Security (ICDIS), pages 43–47.

Alvari, H., Shaabani, E., Sarkar, S., Beigi, G., and Shakarian, P. (2019b). Less is more: Semi-supervised causal inference for detecting pathogenic users in social media. In Companion Proceedings of The 2019 World Wide Web Conference, WWW ’19, page 154–161, New York, NY, USA. Association for Computing Machinery.

Ardehaly, E. M. and Culotta, A. (2017). Co-training for demographic classification using deep learning from label proportions. In 2017 IEEE International Conference on Data Mining Workshops (ICDMW), pages 1017–1024.

Balestrucci, A. and De Nicola, R. (2020). Credulous users and fake news: a real case study on the propagation in twitter. In 2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), pages 1–8.

Bin Tareaf, R., Alhosseini, S. A., and Meinel, C. (2019). Facial-based personality prediction models for estimating individuals private traits. In 2019 IEEE Intl Conf on Parallel Distributed Processing with Applications, Big Data Cloud Computing, Sustainable Computing Communications, Social Computing Networking (ISPA/BDCloud/SocialCom/SustainCom), pages 1586–1594.

Biswas, K., Shivakumara, P., Pal, U., Chakraborti, T., Lu, T., and Ayub, M. N. B. (2022). Fuzzy and genetic algorithm based approach for classification of personality traits oriented social media images. Knowledge-Based Systems, 241:108024.

Brereton, P., Kitchenham, B. A., Budgen, D., Turner, M., and Khalil, M. (2007). Lessons from applying the systematic literature review process within the software engineering domain. Journal of Systems and Software, 80(4):571–583. Software Performance.

Brough, M., Literat, I., and Ikin, A. (2020). “good social media?”: Underrepresented youth perspectives on the ethical and equitable design of social media platforms. Social Media + Society, 6(2):2056305120928488.

Burger, J. D., Henderson, J., Kim, G., and Zarrella, G. (2011). Discriminating gender on twitter. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP ’11, page 1301–1309, USA. Association for Computational Linguistics.

Castellini, J., Poggioni, V., and Sorbi, G. (2017). Fake twitter followers detection by denoising autoencoder. In Proceedings of the International Conference on Web Intelligence, WI ’17, page 195–202, New York, NY, USA. Association for Computing Machinery.

Chatfield, A. T., Reddick, C. G., and Choi, K. P. (2017). Online media use of false news to frame the 2016 trump presidential campaign. In Proceedings of the 18th Annual International Conference on Digital Government Research, dg.o ’17, page 213–222, New York, NY, USA. Association for Computing Machinery.

Chen, J., Liu, Y., and Zou, M. (2016). Home location profiling for users in social media. Information Management, 53(1):135–143.

Chen, W.-Y., Chu, J.-C., Luan, J., Bai, H., Wang, Y., and Chang, E. Y. (2009). Collaborative filtering for orkut communities: Discovery of user latent behavior. In Proceedings of the 18th International Conference on World Wide Web, WWW ’09, page 681–690, New York, NY, USA. Association for Computing Machinery.

Choumane, A., Al Abidin Ibrahim, Z., and Chebaro, B. (2017). Correspondência de perfis em redes sociais com base em semelhanças semânticas e relacionamentos comuns. In Proceedings of the International Conference on Compute and Data Analysis, ICCDA ’17, page 14–18, Nova York, NY, EUA. Association for Computing Machinery.

Coletto, M., Lucchese, C., and Orlando, S. (2018). Do violent people smile: Social media analysis of their profile pictures. In Companion Proceedings of the The Web Conference 2018, WWW ’18, page 1465–1468, Republic and Canton of Geneva, CHE. International World Wide Web Conferences Steering Committee.

Correa, D., Sureka, A., and Sethi, R. (2012). Whacky! what anyone could know about you from twitter. In 2012 Tenth Annual International Conference on Privacy, Security and Trust, pages 43–50.

Das, U., Narayanan, A., Gupta, A., Bagga, O. S., and Chopra, S. (2018). Social champion identification for ngos. In 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, pages 361–366.

DataReportal (2023). Digital 2022 - october global. Último acesso em 01 de junho de 2023.

Efstathiades, H., Antoniades, D., Pallis, G., and Dikaiakos, M. D. (2015). Identification of key locations based on online social network activity. In 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 218–225.

Elmendili, F., Chaoui, H., and Bouzekri El Idrissi, Y. E. (2019). Social Network’s Security Related to Healthcare, pages 91–113.

Faralli, S., Stilo, G., and Velardi, P. (2017). Automatic acquisition of a taxonomy of microblogs users’ interests. Journal of Web Semantics, 45:23–40.

Felizardo, K. R., Nakagawa, E. Y., Fabbri, S. C. P. F., and Ferrari, F. C. (2017). Revisão sistemárica da literatura em engenharia de software: teoria e prática. Elsevier.

Fernández, D., Moctezuma, D., and Siordia, O. S. (2016). Features combination for gender recognition on twitter users. In 2016 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), pages 1–6.

Firdaus, S. N., Ding, C., and Sadeghian, A. (2021). Retweet prediction based on topic, emotion and personality. Online Social Networks and Media, 25:100165.

Gao, M., Lim, E.-P., Lo, D., Zhu, F., Prasetyo, P. K., and Zhou, A. (2015). Cnl: Collective network linkage across heterogeneous social platforms. In 2015 IEEE International Conference on Data Mining, pages 757–762.

Golbeck, J., Robles, C., Edmondson, M., and Turner, K. (2011). Predicting personality from twitter. In 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing, pages 149–156.

Google (2023). Pesquisa realizada em 20 de abril de 2023.

Gupta, A., Joshi, A., and Kumaraguru, P. (2012). Identifying and characterizing user communities on twitter during crisis events. In Proceedings of the 2012 Workshop on Data-Driven User Behavioral Modelling and Mining from Social Media, DUBMMSM ’12, page 23–26, New York, NY, USA. Association for Computing Machinery.

Gutierrez, F. J. and Poblete, B. (2015). Sentiment-based user profiles in microblogging platforms. In Proceedings of the 26th ACM Conference on Hypertext & Social Media, HT ’15, page 23–32, New York, NY, USA. Association for Computing Machinery.

Han Veiga, M. and Eickhoff, C. (2016). A cross-platform collection of social network profiles. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’16, page 665–668, New York, NY, USA. Association for Computing Machinery.

Harrison, J., Bell, E., Corley, C., Dowling, C., and Cowell, A. (2015). Assessment of user home location geoinference methods. In 2015 IEEE International Conference on Intelligence and Security Informatics (ISI), pages 148–150.

Hayama, T. (2022). Analyzing features of passive twitter’s users to estimate passive twitter-user’s interests. In IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT ’21, page 476–481, New York, NY, USA. Association for Computing Machinery.

He, S., Wang, H., and Jiang, Z. H. (2014a). Identifying user behavior on twitter based on multi-scale entropy. In Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), pages 381–384.

He, S., Wang, H., and Jiang, Z. H. (2014b). Identifying user behavior on twitter based on multi-scale entropy. In Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), pages 381–384.

Heidari, M., Jones, J. H., and Uzuner, O. (2020). Deep contextualized word embedding for text-based online user profiling to detect social bots on twitter. In 2020 International Conference on Data Mining Workshops (ICDMW), pages 480–487.

Herzig, J., Mass, Y., and Roitman, H. (2014). An author-reader influence model for detecting topic-based influencers in social media. In Proceedings of the 25th ACM Conference on Hypertext and Social Media, HT ’14, page 46–55, New York, NY, USA. Association for Computing Machinery.

Huang, W., Weber, I., and Vieweg, S. (2014). Inferring nationalities of twitter users and studying inter-national linking. In Proceedings of the 25th ACM Conference on Hypertext and Social Media, HT ’14, page 237–242, New York, NY, USA. Association for Computing Machinery.

Humadde, H. S., Abdul-Hassan, A. K., and Mahdi, B. S. (2019). Proposed user identification algorithm across social network using hybrid techniques. In 2019 2nd Scientific Conference of Computer Sciences (SCCS), pages 158–161.

Huynh, H. N., Legara, E. F., and Monterola, C. (2015). A dynamical model of twitter activity profiles. In Proceedings of the 26th ACM Conference on Hypertext & Social Media, HT ’15, page 49–57, New York, NY, USA. Association for Computing Machinery.

Ikeda, K., Hattori, G., Ono, C., Asoh, H., and Higashino, T. (2013). Twitter user profiling based on text and community mining for market analysis. Knowledge-Based Systems, 51:35–47.

Jain, P., Kumaraguru, P., and Joshi, A. (2013). @i seek ’fb.me’: Identifying users across multiple online social networks. In Proceedings of the 22nd International Conference on World Wide Web, WWW ’13 Companion, page 1259–1268, New York, NY, USA. Association for Computing Machinery.

Jain, P., Kumaraguru, P., and Joshi, A. (2015). Other times, other values: Leveraging attribute history to link user profiles across online social networks. In Proceedings of the 26th ACM Conference on Hypertext & Social Media, HT ’15, page 247–255, New York, NY, USA. Association for Computing Machinery.

Kamei, F., Wiese, I., Lima, C., Polato, I., Nepomuceno, V., Ferreira, W., Ribeiro, M., Pena, C., Cartaxo, B., Pinto, G., and Soares, S. (2021). Grey literature in software engineering: A critical review. Information and Software Technology, 138:106609.

Kantepe, M. and Ganiz, M. C. (2017). Preprocessing framework for twitter bot detection. In 2017 International Conference on Computer Science and Engineering (UBMK), pages 630–634.

Karamitsos, I., Mohasseb, A., and Kanavos, A. (2022). A graph mining method for characterizing and measuring user engagement in twitter. In 2022 17th International Workshop on Semantic and Social Media Adaptation Personalization (SMAP), pages 1–6.

Kasbekar, P., Potika, K., and Pollett, C. (2020). Find me if you can: Aligning users in different social networks. In 2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService), pages 46–53.

Kashyap, R. and Nahapetian, A. (2014). Tweet analysis for user health monitoring. In 2014 4th International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare Through Innovations in Mobile and Wireless Technologies (MOBIHEALTH), pages 348–351.

Kaubiyal, J. and Jain, A. K. (2019). A feature based approach to detect fake profiles in twitter. In Proceedings of the 3rd International Conference on Big Data and Internet of Things, BDIOT 2019, page 135–139, New York, NY, USA. Association for Computing Machinery.

Kim, S. M., Paris, C., Power, R., and Wan, S. (2017). Distinguishing individuals from organisations on twitter. WWW ’17 Companion, page 805–806, Republic and Canton of Geneva, CHE. International World Wide Web Conferences Steering Committee.

Kitchenham, B. and Charters, S. (2007). Guidelines for Performing Systematic Literature Reviews in Software Engineering.

Kostakos, P., Pandya, A., Kyriakouli, O., and Oussalah, M. (2018). Inferring demographic data of marginalized users in twitter with computer vision apis. In 2018 European Intelligence and Security Informatics Conference (EISIC), pages 81–84.

Kotzias, D., Lappas, T., and Gunopulos, D. (2016). Home is where your friends are: Utilizing the social graph to locate twitter users in a city. Information Systems, 57:77–87.

Kumar, S., Hu, X., and Liu, H. (2014). A behavior analytics approach to identifying tweets from crisis regions. In Proceedings of the 25th ACM Conference on Hypertext and Social Media, HT ’14, page 255–260, New York, NY, USA. Association for Computing Machinery.

Kwon, S., Liang, P., Tandon, S., Berman, J., Chang, P.-j., and Gilbert, E. (2018). Tweety holmes: A browser extension for abusive twitter profile detection. In Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW ’18, page 17–20, New York, NY, USA. Association for Computing Machinery.

Li, R., Wang, S., Deng, H., Wang, R., and Chang, K. C.-C. (2012). Towards social user profiling: Unified and discriminative influence model for inferring home locations. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’12, page 1023–1031, New York, NY, USA. Association for Computing Machinery.

Ma, X., Tsuboshita, Y., and Kato, N. (2014). Gender estimation for sns user profiling using automatic image annotation. In 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pages 1–6.

Malhotra, A., Totti, L., Meira Jr., W., Kumaraguru, P., and Almeida, V. (2012). Studying user footprints in different online social networks. In 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pages 1065–1070.

Masood, M. A. and Abbasi, R. A. (2021). Using graph embedding and machine learning to identify rebels on twitter. Journal of Informetrics, 15(1):101121.

Mbarek, A., Jamoussi, S., and Hamadou, A. B. (2022). An across online social networks profile building approach: Application to suicidal ideation detection. Future Generation Computer Systems, 133:171–183.

McGee, J., Caverlee, J., and Cheng, Z. (2013). Location prediction in social media based on tie strength. In Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, CIKM ’13, page 459–468, New York, NY, USA. Association for Computing Machinery.

Merler, M., Cao, L., and Smith, J. R. (2015). You are what you tweet. . . pic! gender prediction based on semantic analysis of social media images. In 2015 IEEE International Conference on Multimedia and Expo (ICME), pages 1–6.

Meshram, S., Babu, R., and Adhikari, J. (2020). Detecting psychological stress using machine learning over social media interaction. In 2020 5th International Conference on Communication and Electronics Systems (ICCES), pages 646–649.

NAJI, M., Najima, D., Hasnae, R., and AJHOUN, R. (2019). Customized data extraction and processing for the prediction of baby blues from social media. In 2019 1st International Conference on Smart Systems and Data Science (ICSSD), pages 1–6.

Narayanan, A., Garg, A., Arora, I., Sureka, T., Sridhar, M., and Prasad, H. (2018). Ironsense: Towards the identification of fake user-profiles on twitter using machine learning. In 2018 Fourteenth International Conference on Information Processing (ICINPRO), pages 1–7.

Nargundkar, A. and Rao, Y. S. (2016). Influencerank: A machine learning approach to measure influence of twitter users. In 2016 International Conference on Recent Trends in Information Technology (ICRTIT), pages 1–6.

Nie, Y., Jia, Y., Li, S., Zhu, X., Li, A., and Zhou, B. (2016). Identifying users across social networks based on dynamic core interests. Neurocomputing, 210:107–115. SI:Behavior Analysis In SN.

Pakaya, F. N., Ibrohim, M. O., and Budi, I. (2019). Malicious account detection on twitter based on tweet account features using machine learning. In 2019 Fourth International Conference on Informatics and Computing (ICIC), pages 1–5.

Pathak, A., Madani, N., and Joseph, K. (2021). A method to analyze multiple social identities in twitter bios. 5(CSCW2).

Petticrew, M., R.-H. (2006). Systematic reviews in the social sciences : a practical guide. BLACKWELL PUBLISHING, page 90.

Pham, P., Nguyen, L. T., Vo, B., and Yun, U. (2022). Bot2vec: A general approach of intra-community oriented representation learning for bot detection in different types of social networks. Information Systems, 103:101771.

Phand, S. A. and Phand, J. A. (2017). Twitter sentiment classification using stanford nlp. In 2017 1st International Conference on Intelligent Systems and Information Management (ICISIM), pages 1–5.

Piao, G. (2021). A simple language independent approach for distinguishing individuals on social media. HT ’21, page 251–256, New York, NY, USA. Association for Computing Machinery.

Pizarro, J. (2020). Profiling bots and fake news spreaders at pan’19 and pan’20 : Bots and gender profiling 2019, profiling fake news spreaders on twitter 2020. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pages 626–630.

Poblete, B., Garcia, R., Mendoza, M., and Jaimes, A. (2011). Do all birds tweet the same? characterizing twitter around the world. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM ’11, page 1025–1030, New York, NY, USA. Association for Computing Machinery.

Pratama, R. P. and Maharani, W. (2021). Predicting big five personality traits based on twitter user u sing random forest method. In 2021 International Conference on Data Science and Its Applications (ICoDSA), pages 110–117.

Preotiuc-Pietro, D., Carpenter, J., Giorgi, S., and Ungar, L. (2016). Studying the dark triad of personality through twitter behavior. In Proceedings of the 25th ACM Interna tional on Conference on Information and Knowledge Management, CIKM ’16, page 761–770, New York, NY, USA. Association for Computing Machinery.

Quercia, D., Kosinski, M., Stillwell, D., and Crowcroft, J. (2011). Our twitter profiles, our selves: Predicting personality with twitter. In 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing, pages 180–185.

Roedler, R., Kergl, D., and Rodosek, G. D. (2017). Content driven profile matching across online social networks. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, ASONAM ’17, page 1049–1055, New York, NY, USA. Association for Computing Machinery.

Ruffo, G., Semeraro, A., Giachanou, A., and Rosso, P. (2023). Studying fake news spreading, polarisation dynamics, and manipulation by bots: A tale of networks and language. Computer Science Review, 47:100531.

Sahoo, S. R. and Gupta, B. (2019). Hybrid approach for detection of malicious profiles in twitter. Computers Electrical Engineering, 76:65–81.

Saidi, F., Trabelsi, Z., and Thangaraj, E. (2022). A novel framework for semantic classification of cyber terrorist communities on twitter. Engineering Applications of Artificial Intelligence, 115:105271.

Shah, C., Thacker, C., and Patel, Y. (2022). Multi-label personality prediction on twitter data using machine learning. In 2022 International Mobile and Embedded Technology Conference (MECON), pages 140–144.

Shinde, S. and Mane, S. B. (2021). Malicious profile detection on social media: A survey paper. In 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), pages 1–5.

Si, H., Zhou, J., Chen, Z., Wan, J., Xiong, N. N., Zhang, W., and Vasilakos, A. V. (2019). Association rules mining among interests and applications for users on social networks. IEEE Access, 7:116014–116026.

Siddiqui, H., Healy, E., and Olmsted, A. (2017). Bot or not. In 2017 12th International Conference for Internet Technology and Secured Transactions (ICITST), pages 462–463.

Sowmya, P. and Chatterjee, M. (2020). Detection of fake and clone accounts in twitter using classification and distance measure algorithms. In 2020 International Conference on Communication and Signal Processing (ICCSP), pages 0067–0070.

Spertus, E., Sahami, M., and Buyukkokten, O. (2005). Evaluating similarity measures: A large-scale study in the orkut social network. In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, KDD ’05, page 678–684, New York, NY, USA. Association for Computing Machinery.

Srivastava, D. K. and Roychoudhury, B. (2020). Words are important: A textual content based identity resolution scheme across multiple online social networks. KnowledgeBased Systems, 195:105624.

Suman, C., Naman, A., Saha, S., and Bhattacharyya, P. (2021). A multimodal author profiling system for tweets. IEEE Transactions on Computational Social Systems, 8(6):1407–1416.

Sumner, C., Byers, A., Boochever, R., and Park, G. J. (2012). Predicting dark triad personality traits from twitter usage and a linguistic analysis of tweets. In 2012 11th International Conference on Machine Learning and Applications, volume 2, pages 386–393.

Suriakala, M. and Revathi, S. (2018). Privacy protected system for vulnerable users and cloning profile detection using data mining approaches. In 2018 Tenth International Conference on Advanced Computing (ICoAC), pages 124–132.

Tang, C., Wang, B., Luo, Z., Wu, H., Dasan, S., Fu, M., Li, Y., Ghosh, M., Kabra, R., Navadiya, N. K., Cheng, D., Dai, F., Channapattan, V., and Mishra, P. (2021). Forecasting sql query cost at twitter. In 2021 IEEE International Conference on Cloud Engineering (IC2E), pages 154–160.

Tundis, A. and Mühlhäuser, M. (2017). A multi-language approach towards the identification of suspicious users on social networks. In 2017 International Carnahan Conference on Security Technology (ICCST), pages 1–6.

Twitter (2023). About twitter. Último acesso em 01 de junho de 2023.

Utami, E., Hartanto, A. D., Adi, S., Oyong, I., and Raharjo, S. (2022). Profiling analysis of disc personality traits based on twitter posts in bahasa indonesia. Journal of King Saud University Computer and Information Sciences, 34(2):264–269.

Utami, E., Iskandar, A. F., Hartanto, A. D., and Raharjo, S. (2021). Disc personality classification using twitter: Usability testing. In 2021 IEEE 5th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), pages 180–185.

Velayutham, T. and Tiwari, P. K. (2017). Bot identification: Helping analysts for right data in twitter. In 2017 3rd International Conference on Advances in Computing,Communication Automation (ICACCA) (Fall), pages 1–5.

Volkova, S., Bachrach, Y., and Van Durme, B. (2016). Mining user interests to predict perceived psycho-demographic traits on twitter. In 2016 IEEE Second International Conference on Big Data Computing Service and Applications (BigDataService), pages 36–43.

Wagner, C., Asur, S., and Hailpern, J. (2013). Religious politicians and creative photographers: Automatic user categorization in twitter. In 2013 International Conference on Social Computing, pages 303–310.

Wandabwa, H. M., Naeem, M. A., Mirza, F., and Pears, R. (2021). Topical affinity in short text microblogs. Information Systems, 96:101662.

Wang, T., Brede, M., Ianni, A., and Mentzakis, E. (2017). Detecting and characterizing eating-disorder communities on social media. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, WSDM ’17, page 91–100, New York, NY, USA. Association for Computing Machinery.

Wang, X., Zhang, H., Wang, Z., Qiao, Y., Ma, J., and Dai, H. (2021). Con&net: A cross-network anchor link discovery method based on embedding representation. ACM Trans. Knowl. Discov. Data, 16(2).

Wickramarathna, N. C., Jayasiriwardena, T. D., Wijesekara, M., Munasinghe, P. B., and Ganegoda, G. U. (2020). A framework to detect twitter platform manipulation and computational propaganda. In 2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer), pages 214–219.

Wijesekara, M. and Ganegoda, G. U. (2020). Source credibility analysis on twitter users. In 2020 International Research Conference on Smart Computing and Systems Engineering (SCSE), pages 96–102.

Yamaguchi, Y., Amagasa, T., and Kitagawa, H. (2013). Landmark-based user location inference in social media. In Proceedings of the First ACM Conference on Online Social Networks, COSN ’13, page 223–234, New York, NY, USA. Association for Computing Machinery.

Yamaguchi, Y., Amagasa, T., Kitagawa, H., and Ikawa, Y. (2014). Online user location inference exploiting spatiotemporal correlations in social streams. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM ’14, page 1139–1148, New York, NY, USA. Association for Computing Machinery.

Yazdavar, A. H., Al-Olimat, H. S., Ebrahimi, M., Bajaj, G., Banerjee, T., Thirunarayan, K., Pathak, J., and Sheth, A. (2017). Semi-supervised approach to monitoring clinical depressive symptoms in social media. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, ASONAM ’17, page 1191–1198, New York, NY, USA. Association for Computing Machinery.

Yu, Z., Yi, F., Lv, Q., and Guo, B. (2018). Identifying on-site users for social events: Mobility, content, and social relationship. IEEE Transactions on Mobile Computing, 17(9):2055–2068.

Zarrinkalam, F., Kahani, M., and Bagheri, E. (2018). Mining user interests over active topics on social networks. Information Processing Management, 54(2):339–357.

Zengin Alp, Z. and Şule Gündüz Öğüdücü (2018). Identifying topical influencers on twitter based on user behavior and network topology. Knowledge-Based Systems, 141:211–221.

Zhao, J., Gou, L., Wang, F., and Zhou, M. (2014). Pearl: An interactive visual analytic tool for understanding personal emotion style derived from social media. In 2014 IEEE Conference on Visual Analytics Science and Technology (VAST), pages 203–212.

Zhong, T., Wang, T., Wang, J., Wu, J., and Zhou, F. (2020). Multiple-aspect attentional graph neural networks for online social network user localization. IEEE Access, 8:95223–95234.
Publicado
16/10/2023
OLIVEIRA, João Marcelo Silva de; SANTOS, Isadora Mendes dos; MOTA, Marcelle Pereira. Mapeamento Sistemático da Literatura sobre a Caracterização do Usuário do Twitter. In: WORKSHOP SOBRE ASPECTOS DA INTERAÇÃO HUMANO-COMPUTADOR NA WEB SOCIAL (WAIHCWS), 14. , 2023, Maceió/AL. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 45-63. ISSN 2596-0296. DOI: https://doi.org/10.5753/waihcws.2023.233752.