A language-based approach to modelling and analysis of Twitter interactions. (English) Zbl 1359.68197
Summary: More than a personal microblogging site, Twitter has been transformed by common use to an information publishing venue, which public characters, media channels and common people daily rely on for, e.g., news reporting and consumption, marketing, and social messaging. The use of Twitter in a cooperative and interactive setting calls for the precise awareness of the dynamics regulating message spreading. In this paper, we describe Twitlang, a language for modelling the interactions among Twitter accounts. The associated operational semantics allows users to precisely determine the effects of their actions on Twitter, such as post, reply-to or delete tweets. The language is implemented in the form of a Maude interpreter, Twitlanger, which takes a language term as an input and explores the computations arising from the term. By combining the strength of Twitlanger and the Maude model checker, it is possible to automatically verify communication properties of Twitter accounts. We illustrate the benefits of our executable formalisation by means of an application scenario inspired from real life. While the scenario highlights the benefits of adopting Twitter for a cooperative use in the everyday life, our analysis shows that appropriate settings are essential for a proper usage of the platform, in respect of fulfilling those communication properties expected within collaborative and interactive contexts.
MSC:
| 68Q60 | Specification and verification (program logics, model checking, etc.) |
| 91D30 | Social networks; opinion dynamics |
References:
| [1] | De Nicola, R.; Maggi, A.; Petrocchi, M.; Spognardi, A.; Tiezzi, F., Twitlang(er): interactions modeling language (and interpreter) for Twitter, (Proceedings Software Engineering and Formal Methods - 13th International Conference. Proceedings Software Engineering and Formal Methods - 13th International Conference, SEFM 2015, York, UK, September 7-11, 2015 (2015)), 327-343 |
| [2] | Smith, C., 170 amazing Twitter statistics and facts. DMR (November 2016) |
| [3] | Brandwatch.com, Brands on Twitter 2013: brandwatch report (2013), last checked December 18, 2015 |
| [4] | Save the Children, It’s hurricane season. Is your family prepared in case of an emergency? (2014), last checked December 18, 2015 |
| [5] | Myers, S. A.; Sharma, A.; Gupta, P.; Lin, J., Information network or social network?: The structure of the Twitter follow graph, (WWW (2014), ACM), 493-498 |
| [6] | Ritter, A.; Cherry, C.; Dolan, B., Unsupervised modeling of Twitter conversations, (HLT-NAACL (2010)), 172-180 |
| [7] | Milner, R., Communication and Concurrency (1989), Prentice-Hall · Zbl 0683.68008 |
| [8] | Plotkin, G., A structural approach to operational semantics, J. Log. Algebraic Program., 60-61, 17-139 (2004) · Zbl 1082.68062 |
| [9] | Milner, R.; Parrow, J.; Walker, D., A calculus of mobile processes, Inf. Comput., 100, 1, 1-77 (1992) · Zbl 0752.68037 |
| [10] | Clavel, M.; Durán, F.; Eker, S.; Lincoln, P.; Martí-Oliet, N.; Meseguer, J.; Talcott, C., All About Maude - A High-performance Logical Framework (2007), Springer · Zbl 1115.68046 |
| [11] | Verdejo, A.; Martí-Oliet, N., Implementing CCS in Maude 2, (WRLA. WRLA, ENTCS, vol. 71 (2002), Elsevier), 239-257 |
| [12] | Eker, S.; Meseguer, J.; Sridharanarayanan, A., The Maude LTL model checker, Electron. Notes Theor. Comput. Sci., 71, 162-187 (2002) · Zbl 1272.68243 |
| [13] | Rossi, L.; Magnani, M., Conversation practices and network structure in Twitter, (Sixth International Conference on Weblogs and Social Media ICWSM (2012)) |
| [14] | Magnani, M.; Rossi, L., The ML-model for multi-layer social networks, (ASONAM (2011)), 5-12 |
| [15] | Abel, F.; Gao, Q.; Houben, G.-J.; Tao, K., Analyzing user modeling on Twitter for personalized news recommendations, (User Modeling, Adaption and Personalization (2011), Springer), 1-12 |
| [16] | Abel, F.; Hauff, C.; Houben, G.-J.; Stronkman, R.; Tao, K., Twitcident: fighting fire with information from social web streams, (WWW (2012)), 305-308 |
| [17] | Mendoza, M.; Poblete, B.; Castillo, C., Twitter under crisis: can we trust what we RT?, (SOMA (2010), ACM), 71-79 |
| [18] | Laniado, D.; Mika, P., Making sense of Twitter, (ISWC, vol. 1 (2010)), 470-485 |
| [19] | Gonçalves, B.; Perra, N.; Vespignani, A., Modeling users’ activity on Twitter networks: validation of Dunbar’s number, PLoS ONE, 6, 8 (2011) |
| [20] | Rosenthal, S.; Nakov, P.; Kiritchenko, S.; Mohammad, S.; Ritter, A.; Stoyanov, V., SemEval-2015 task 10: sentiment analysis in Twitter, (9th International Workshop on Semantic Evaluation (2015), Association for Computational Linguistics), 451-463 |
| [21] | Basile, P.; Novielli, N., UNIBA: sentiment analysis of English tweets combining micro-blogging, lexicon and semantic features, (9th International Workshop on Semantic Evaluation (SemEval 2015) (2015), Association for Computational Linguistics), 595-600 |
| [22] | Bollen, J.; Mao, H.; Pepe, A., Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena, (ICWSM (2011)) |
| [23] | Pak, A.; Paroubek, P., Twitter as a corpus for sentiment analysis and opinion mining, (Seventh Conference on International Language Resources and Evaluation (2010), ELRA) |
| [24] | Hong, L.; Davison, B. D., Empirical study of topic modeling in Twitter, (SOMA (2010), ACM), 80-88 |
| [25] | Larson, D., 9 strange things about tweets, retweets and DMs every Twitter user must know (2011), last checked December 18, 2015 |
| [26] | Stringhini, G.; Kruegel, C.; Vigna, G., Detecting spammers on social networks, (ACSAC (2010), ACM), 1-9 |
| [27] | Yang, C.; Harkreader, R.; Gu, G., Empirical evaluation and new design for fighting evolving Twitter spammers, IEEE Trans. Inf. Forensics Secur., 8, 8, 1280-1293 (2013) |
| [28] | Cresci, S.; Di Pietro, R.; Petrocchi, M.; Spognardi, A.; Tesconi, M., A criticism to society (as seen by Twitter analytics), (34th International Conference on Distributed Computing Systems Workshops. 34th International Conference on Distributed Computing Systems Workshops, ICDCS 2014 Workshops (DASec) (2014), IEEE), 194-200 |
| [29] | Cresci, S.; Di Pietro, R.; Petrocchi, M.; Spognardi, A.; Tesconi, M., Fame for sale: efficient detection of fake Twitter followers, Decis. Support Syst., 80, 56-71 (2015) |
| [30] | Viswanath, B.; Bashir, M. A.; Zafar, M. B.; Bouget, S.; Guha, S.; Gummadi, K. P.; Kate, A.; Mislove, A., Strength in numbers: robust tamper detection in crowd computations, (ACM on Conference on Online Social Networks. ACM on Conference on Online Social Networks, COSN ’15 (2015), ACM), 113-124 |
| [31] | Cresci, S.; Pietro, R. D.; Petrocchi, M.; Spognardi, A.; Tesconi, M., DNA-inspired online behavioral modeling and its application to spambot detection, IEEE Intell. Syst., 31, 5, 58-64 (2016) |
| [32] | Fong, P. W.L.; Anwar, M.; Zhao, Z., A privacy preservation model for Facebook-style social network systems, (Proceedings Computer Security - ESORICS 2009: 14th European Symposium on Research in Computer Security. Proceedings Computer Security - ESORICS 2009: 14th European Symposium on Research in Computer Security, Saint-Malo, France, September 21-23, 2009 (2009), Springer Berlin Heidelberg: Springer Berlin Heidelberg Berlin, Heidelberg), 303-320 |
| [33] | Fong, P. W., Relationship-based access control: protection model and policy language, (Proceedings of the First ACM Conference on Data and Application Security and Privacy. Proceedings of the First ACM Conference on Data and Application Security and Privacy, CODASPY ’11 (2011), ACM), 191-202 |
| [34] | Pardo, R.; Schneider, G., A formal privacy policy framework for social networks, (Proceedings Software Engineering and Formal Methods: 12th International Conference. Proceedings Software Engineering and Formal Methods: 12th International Conference, SEFM 2014, Grenoble, France, September 1-5, 2014 (2014), Springer International Publishing), 378-392 |
| [35] | Seligman, J.; Liu, F.; Girard, P., Facebook and the epistemic logic of friendship, (Proceedings of the 14th Conference on Theoretical Aspects of Rationality and Knowledge. Proceedings of the 14th Conference on Theoretical Aspects of Rationality and Knowledge, TARK 2013, Chennai, India, January 7-9, 2013 (2013)) |
| [36] | De Nicola, R.; Ferrari, G.; Pugliese, R., KLAIM: a kernel language for agents interaction and mobility, IEEE Trans. Softw. Eng., 24, 5, 315-330 (1998) |
| [37] | De Nicola, R.; Loreti, M.; Pugliese, R.; Tiezzi, F., A formal approach to autonomic systems programming: the SCEL language, ACM Trans. Auton. Adapt. Syst., 9, 2 (2014) |
| [38] | Sebastio, S.; Vandin, A., MultiVeStA: statistical model checking for discrete event simulators, (ValueTools (2013), ICST/ACM), 310-315 |
| [39] | Bae, K.; Meseguer, J., Model checking linear temporal logic of rewriting formulas under localized fairness, Sci. Comput. Program., 99, 193-234 (2015) |
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