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A distributed argumentation algorithm for mining consistent opinions in weighted Twitter discussions. (English) Zbl 1418.91423

Summary: Twitter is one of the most powerful social media platforms, reflecting both support and contrary opinions among people who use it. In a recent work, we developed an argumentative approach for analyzing the major opinions accepted and rejected in Twitter discussions. A Twitter discussion is modeled as a weighted argumentation graph where each node denotes a tweet, each edge denotes a relationship between a pair of tweets of the discussion and each node is attached to a weight that denotes the social relevance of the corresponding tweet in the discussion. In the social network Twitter, a tweet always refers to previous tweets in the discussion, and therefore the underlying argument graph obtained is acyclic. However, when in a discussion we group the tweets by author, the graph that we obtain can contain cycles. Based on the structure of graphs, in this work we introduce a distributed algorithm to compute the set of globally accepted opinions of a Twitter discussion based on valued argumentation. To understand the usefulness of our distributed algorithm, we study cases of argumentation graphs that can be solved efficiently with it. Finally, we present an experimental investigation that shows that when solving acyclic argumentation graphs associated with Twitter discussions our algorithm scales at most with linear time with respect to the size of the discussion. For argumentation graphs with cycles, we study tractable cases and we analyze how frequent are these cases in Twitter. Moreover, for the non-tractable cases we analyze how close is the solution of the distributed algorithm with respect to the one computed with the general sequential algorithm, that we have previously developed, that solves any argumentation graph.

MSC:

91D30 Social networks; opinion dynamics
68W15 Distributed algorithms

Software:

Pregel; ASPARTIX
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Full Text: DOI Link

References:

[1] Alsinet T, Argelich J, Béjar R, Esteva F, Godo L (2017a) A probabilistic author-centered model for Twitter discussions. In: IJCAI workshop on logical foundations for uncertainty and machine learning, pp 3-8
[2] Alsinet T, Argelich J, Béjar R, Fernández C, Mateu C, Planes J (2017b) Weighted argumentation for analysis of discussions in Twitter. Int J Approx Reason 85:21-35 · Zbl 1422.91606 · doi:10.1016/j.ijar.2017.02.004
[3] Alsinet T, Argelich J, Béjar R, Planes J, Cemeli J, Sanahuja C (2017c) A distributed approach for the analysis of discussions in twitter. In: Proceedings of the 3rd international workshop on social influence analysis co-located with 26th international joint conference on artificial intelligence (IJCAI 2017), Melbourne, Australia, August 19, 2017, pp 45-56
[4] Alsinet T, Argelich J, Béjar R, Fernández C, Mateu C, Planes J (2018) An argumentative approach for discovering relevant opinions in Twitter with probabilistic valued relationships. Pattern Recogn Lett 105:191-199. https://doi.org/10.1016/j.patrec.2017.07.004 · doi:10.1016/j.patrec.2017.07.004
[5] Baroni P, Giacomin M (2001) A distributed self-stabilizing algorithm for argumentation. In: Proceedings of the 15th international parallel and distributed processing symposium (IPDPS-01), IEEE Computer Society, p 79 · Zbl 1031.68120
[6] Baroni P, Giacomin M (2002) Argumentation through a distributed self-stabilizing approach. J Exp Theor Artif Intell 14(4):273-301 · Zbl 1031.68120 · doi:10.1080/09528130110116642
[7] Bench-Capon TJM (2002) Value-based argumentation frameworks. In: Proceedings of 9th international workshop on non-monotonic reasoning, NMR 2002, pp 443-454
[8] Bench-Capon TJM (2003) Persuasion in practical argument using value-based argumentation frameworks. J Log Comput 13(3):429-448 · Zbl 1043.03026 · doi:10.1093/logcom/13.3.429
[9] Bench-Capon TJM, Dunne PE (2007) Argumentation in artificial intelligence. Artif Intell 171(10-15):619-641 · Zbl 1168.68560 · doi:10.1016/j.artint.2007.05.001
[10] Besnard P, Hunter A (2001) A logic-based theory of deductive arguments. Artif Intell 128(1-2):203-235 · Zbl 0971.68143 · doi:10.1016/S0004-3702(01)00071-6
[11] Bild DR, Liu Y, Dick RP, Mao ZM, Wallach DS (2015) Aggregate characterization of user behavior in Twitter and analysis of the retweet graph. ACM Trans Internet Technol 15(1):41-424 · doi:10.1145/2700060
[12] Bosc T, Cabrio E, Villata S (2016) Tweeties squabbling: positive and negative results in applying argument mining on social media. Comput Models Argum-Proc COMMA 2016:21-32
[13] Budán MCD, Simari GI, Simari GR (2016) Using argument features to improve the argumentation process. In: Proceedings of COMMA 2016 Computational Models of Argument, Potsdam, Germany, 12-16 September, 2016, pp 151-158
[14] Caminada M (2007) Comparing two unique extension semantics for formal argumentation: ideal and eager. In: Proceedings of 19th Belgian-Dutch conference on artificial intelligence (BNAIC 2007), pp 81-87
[15] Dung PM (1995) On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artif Intell 77(2):321-357 · Zbl 1013.68556 · doi:10.1016/0004-3702(94)00041-X
[16] Dung PM, Mancarella P, Toni F (2007) Computing ideal sceptical argumentation. Artif Intell 171(10-15):642-674 · Zbl 1168.68564 · doi:10.1016/j.artint.2007.05.003
[17] Dunne PE (2007) Computational properties of argument systems satisfying graph-theoretic constraints. Artif Intell 171(10-15):701-729 · Zbl 1168.68565 · doi:10.1016/j.artint.2007.03.006
[18] Dunne PE (2008) The computational complexity of ideal semantics I: abstract argumentation frameworks. In: Proceedings of computational models of argument, COMMA 2008, Toulouse, France, pp 147-158
[19] Dunne PE (2009) The computational complexity of ideal semantics. Artif Intell 173(18):1559-1591 · Zbl 1185.68666 · doi:10.1016/j.artint.2009.09.001
[20] Dunne PE, Bench-Capon T (2001) Complexity and combinatorial properties of argument systems. Tech. rep., University of Liverpool. http://www.csc.liv.ac.uk/ ped/papers/csd_rep_argument.ps · Zbl 1111.68673
[21] Dusmanu M, Cabrio E, Villata S (2017) Argument mining on twitter: arguments, facts and sources. In: Proceedings of the 2017 conference on empirical methods in natural language processing, EMNLP 2017, pp 2317-2322
[22] Dvorák W, Ordyniak S, Szeider S (2012) Augmenting tractable fragments of abstract argumentation. Artif Intell 186:157-173. https://doi.org/10.1016/j.artint.2012.03.002 · Zbl 1251.68225 · doi:10.1016/j.artint.2012.03.002
[23] Egly U, Gaggl SA, Woltran S (2008) Aspartix: implementing argumentation frameworks using answer-set programming. In: Proceedings of the 24th international conference on logic programming, ICLP 2008, pp 734-738 · Zbl 1226.68018
[24] Fazzinga B, Flesca S, Parisi F (2013) On the complexity of probabilistic abstract argumentation. In: IJCAI 2013, Proceedings of the 23rd international joint conference on artificial intelligence, pp 898-904. IJCAI/AAAI · Zbl 1354.68253
[25] Grosse K, Chesñevar CI, Maguitman AG (2012) An argument-based approach to mining opinions from Twitter. In: Proceedings of the first international conference on agreement technologies, AT 2012, CEUR Workshop Proceedings, vol 918, pp 408-422. CEUR-WS.org
[26] Grosse K, González MP, Chesñevar CI, Maguitman AG (2015) Integrating argumentation and sentiment analysis for mining opinions from Twitter. AI Commun 28(3):387-401 · Zbl 1373.68322 · doi:10.3233/AIC-140627
[27] Hunter A (2012) Some foundations for probabilistic abstract argumentation. In: Computational Models of Argument-Proceedings of COMMA 2012, Frontiers in Artificial Intelligence and Applications, vol 245, pp 117-128. IOS Press
[28] Hunter A (2014) Probabilistic qualification of attack in abstract argumentation. Int J Approx Reason 55(2):607-638 · Zbl 1316.68153 · doi:10.1016/j.ijar.2013.09.002
[29] Li H, Oren N, Norman TJ (2011) Probabilistic argumentation frameworks. In: Theory and applications of formal argumentation – first international workshop, TAFA 2011, Lecture Notes in Computer Science, vol 7132, pp 1-16. Springer, Berlin
[30] Malewicz G, Austern MH, Bik AJC, Dehnert JC, Horn I, Leiser N, Czajkowski G (2010) Pregel: a system for large-scale graph processing. In: Proceedings of the ACM SIGMOD international conference on management of data, SIGMOD 2010, pp 135-146
[31] Rahwan I, Simari GR (2009) Argumentation in artificial intelligence, 1st edn. Springer Publishing Company, Berlin
[32] Thimm M (2012) A probabilistic semantics for abstract argumentation. In: ECAI 2012-20th European conference on artificial intelligence, frontiers in artificial intelligence and applications, vol 242, pp 750-755. IOS Press · Zbl 1327.68290
[33] Valiant LG (2011) A bridging model for multi-core computing. J Comput Syst Sci 77(1):154-166 · Zbl 1210.68134 · doi:10.1016/j.jcss.2010.06.012
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