Abstract
People are becoming increasingly more connected to each other as social networks continue to grow both in number and variety, and this is true for autonomous software agents as well. Taking them as a collection, such social platforms can be seen as one complex network with many different types of relations, different degrees of strength for each relation, and a wide range of information on each node. In this context, social media posts made by users are reflections of the content of their own individual (or local) knowledge bases; modeling how knowledge flows over the network—or how this can possibly occur—is therefore of great interest from a knowledge representation and reasoning perspective. In this article, we provide a formal introduction to the network knowledge base model, and then focus on the problem of how a single agent’s knowledge base changes when exposed to a stream of news items coming from other members of the network. We do so by taking the classical belief revision approach of first proposing desirable properties for how such a local operation should be carried out (theoretical characterization), arriving at three different families of local operators, exploring concrete algorithms (algorithmic characterization) for two of the families, and proving properties about the relationship between the two characterizations (representation theorem). One of the most important differences between our approach and the classical models of belief revision is that in our case the input is more complex, containing additional information about each piece of information.
- [1] . 1985. On the logic of theory change: Partial meet contraction and revision functions. J. Symbol. Logic 50, 2 (1985), 510–530.Google ScholarCross Ref
- [2] . 2017. Social media and fake news in the 2016 election. J. Econ. Perspect. 31, 2 (2017), 211.Google ScholarCross Ref
- [3] . 2015. Main concepts, state of the art and future research questions in sentiment analysis. Acta Polytechn. Hung. 12, 3 (2015), 87–108.Google Scholar
- [4] . 2017. A consensus approach to the sentiment analysis problem driven by support-based IOWA majority. Int. J. Intell. Syst. 32, 9 (2017), 947–65.Google ScholarCross Ref
- [5] . 1964. Weighted voting doesn’t work: A mathematical analysis. Rutgers L. Rev. 19 (1964), 317.Google Scholar
- [6] . 2004. The architecture of complex weighted networks. Proc. Natl. Acad. Sci. U.S.A. 101, 11 (2004), 3747–3752.Google ScholarCross Ref
- [7] . 2014. The structure and dynamics of multilayer networks. Phys. Rep. 544, 1 (2014), 1–122.Google ScholarCross Ref
- [8] . 2001.
A negotiation-style framework for non-prioritised revision . In TARK. Morgan Kaufmann Publishers Inc., 137–150. Google ScholarDigital Library - [9] . 2006. Social contraction and belief negotiation. Inf. Fus. 7, 1 (2006), 19–34. Google ScholarDigital Library
- [10] . 2004.
Logic-based merging: The infinite case . In NMR. 100–108.Google Scholar - [11] . 2006. Complexity of constructing solutions in the core based on synergies among coalitions. Artif. Intell. 170, 6–7 (2006), 607–619. Google ScholarDigital Library
- [12] . 2015.
The spider-man behavior protocol: Exploring both public and dark social networks for fake identity detection in terrorism informatics . In KDWeb. 77–88.Google Scholar - [13] . 1997. On the logic of iterated belief revision. Artif. Intell. 89, 1–2 (1997), 1–29. Google ScholarDigital Library
- [14] . 2017. Marketing through instagram influencers: the impact of number of followers and product divergence on brand attitude. Int. J. Advert. 36, 5 (2017), 798–828.Google ScholarCross Ref
- [15] . 2007.
Belief change based on global minimisation . In IJCAI, Vol. 7. 2468–2473. Google ScholarDigital Library - [16] . 2009. On the computational complexity of weighted voting games. Ann. Math. Artif. Intell. 56, 2 (2009), 109–131. Google ScholarDigital Library
- [17] . 2010. Disjunctive merging: Quota and gmin merging operators. Artif. Intell. 174, 12 (2010), 824–849. Google ScholarDigital Library
- [18] . 2012. Prioritized and non-prioritized multiple change on belief bases. J. Philos. Logic 41, 1 (2012), 77–113.Google ScholarCross Ref
- [19] . 2002. Belief revision, explanations and defeasible reasoning. Artif. Intell. J. 141 (2002), 1–28. Google ScholarDigital Library
- [20] . 1999. Selective revision. Stud. Logic. 63, 3 (1999), 331–342.Google ScholarCross Ref
- [21] . 1997. An Essay on Contraction. Studies in Logic, Language and Information. CSLI Publications, Stanford, CA.Google Scholar
- [22] . 2015.
A desiderata for modeling and reasoning with social knowledge . In CACIC.Google Scholar - [23] . 2016.
Belief dynamics in complex social networks . In ASAI’16.Google Scholar - [24] . 2020. Predicting user reactions to Twitter feed content based on personality type and social cues. Fut. Gener. Comput. Syst. 110 (2020), 918–930.Google ScholarCross Ref
- [25] . 2017. Reasoning about sentiment and knowledge diffusion in social networks. IEEE Internet Comput. 21, 6 (2017), 8–17.Google ScholarCross Ref
- [26] . 2017. A first approach to belief dynamics in complex social networks. In AMW (CEUR Workshop Proceedings), Vol. 1912.Google Scholar
- [27] . 1988. Knowledge in Flux: Modeling the Dynamics of Epistemic States.The MIT Press.Google Scholar
- [28] . 2007. Conciliation through iterated belief merging. J. Logic Comput. 17, 5 (2007), 909–937. Google ScholarDigital Library
- [29] . 2009. Trust and nuanced profile similarity in online social networks. ACM Trans. Web 3, 4 (2009), 12. Google ScholarDigital Library
- [30] . 1997. Semi-revision. J. Appl. Non-Classic. Logics 7, 1–2 (1997), 151–175.Google ScholarCross Ref
- [31] . 1993. Reversing the Levi identity. J. Philos. Logic 22, 6 (1993), 637–669.Google ScholarCross Ref
- [32] . 1994. Kernel contraction. J. Symbol. Logic 59, 3 (1994), 845–859. Google ScholarDigital Library
- [33] . 1999. A Textbook of Belief Dymanics: Theory Change and Database Updating. Kluwer Academic Publishers.Google ScholarCross Ref
- [34] . 2001. Credibility limited revision. J. Symbol. Logic 66, 4 (2001), 1581–1596.Google ScholarCross Ref
- [35] . 2015. Handbook of Social Choice and Voting. Edward Elgar Publishing.Google ScholarCross Ref
- [36] . 2015.
Trust-sensitive belief revision . In IJCAI. 3062–3068. Google ScholarDigital Library - [37] . 2014. Multilayer networks. J. Complex Netw. 2, 3 (2014), 203–271.Google ScholarCross Ref
- [38] . 2002. Merging information under constraints: A logical framework. J. Logic Comput. 12, 5 (2002), 773–808.Google ScholarCross Ref
- [39] . 1998. On the logic of merging. In KR’98. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 488–498. Google ScholarDigital Library
- [40] . 2016. Here’s How Facebook Actually Won Trump the Presidency. Retrieved February 14, 2018 from DOI: https://www.wired.com/2016/11/facebook-won-trump-election-not-just-fake-news/Google Scholar
- [41] . 2008. Tastes, ties, and time: A new (cultural, multiplex, and longitudinal) social network dataset using facebook.com. Soc. Netw. 30 (2008), 330–342.Google ScholarCross Ref
- [42] . 1998. Arbitration (or how to merge knowledge bases). IEEE Trans. Knowl. Data Eng. 10, 1 (1998), 76–90. Google ScholarDigital Library
- [43] . 2014. Logical dynamics of belief change in the community. Synthese 191, 11 (2014), 2403–2431.Google ScholarCross Ref
- [44] . 2017. A trust induced recommendation mechanism for reaching consensus in group decision making. Knowl.-Bas. Syst. 119 (2017), 221–231. Google ScholarDigital Library
- [45] . 2017. More positive, assertive and forward-looking: How leave won Twitter. LSE Brexit (2017).Google Scholar
- [46] . 2014. Weighted multiplex networks. PloS One 9, 6 (2014), e97857.Google ScholarCross Ref
- [47] . 1994. Iterated belief change based on epistemic entrenchment. Erkenntnis 41, 3 (1994), 353–390.Google ScholarCross Ref
- [48] . 2017. The age of Twitter: Donald J. Trump and the politics of debasement. Crit. Stud. Media Commun. 34, 1 (2017), 59–68.Google ScholarCross Ref
- [49] . 2017.
Sensing social media: A range of approaches for sentiment analysis . In Cyberemotions. Springer, 97–117.Google ScholarCross Ref - [50] . 2008. Belief revision. Found. Artif. Intell. 3 (2008), 317–359.Google ScholarCross Ref
- [51] . 2011. A short introduction to preferences: between artificial intelligence and social choice. Synth. Lect. Artif. Intell. Mach. Learn. 5, 4 (2011), 1–102. Google ScholarDigital Library
- [52] . 2015.
Belief revision games. . In AAAI, Vol. 15. 1590–1596. Google ScholarDigital Library - [53] . 2011. Logic in the community. Ind. Conf. Logic Appl. 6521 (2011), 178–188. Google ScholarDigital Library
- [54] . 2013.
Facebook and the epistemic logic of friendship . In TARK.Google Scholar - [55] . 2013. Using generalized annotated programs to solve social network diffusion optimization problems. ACM Trans. Comput. Logic 14, 2 (2013), 10:1–10:40. Google ScholarDigital Library
- [56] . 2016.
Hoaxy: A platform for tracking online misinformation . In WWW Companion. 745–750. Google ScholarDigital Library - [57] . 1954. A method for evaluating the distribution of power in a committee system. Am. Pol. Sci. Rev. 48, 3 (1954), 787–792.Google ScholarCross Ref
- [58] . 2012. Prioritized repairing and consistent query answering in relational databases. Ann. Math. Artif. Intell. 64, 2 (
01 Mar. 2012), 209–246. Google ScholarDigital Library - [59] . 2014. On the revision of informant credibility orders. Artif. Intell. 212 (2014), 36–58. Google ScholarDigital Library
- [60] . 2016. Towards Network False Identity Detection in Online Social Networks. Ph.D. Dissertation. Southern Illinois University at Edwardsville.Google Scholar
- [61] . 2011. Practical aggregation operators for gradual trust and distrust. Fuzzy Sets Syst. 184, 1 (2011), 126–147. Google ScholarDigital Library
- [62] . 2017. A visual interaction consensus model for social network group decision making with trust propagation. Knowl.-Bas. Syst. 122 (2017), 39–50. Google ScholarDigital Library
- [63] . 1988. On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Trans. Syst. Man Cybernet. 18, 1 (1988), 183–190. Google ScholarDigital Library
- [64] . 2018. Recent trends in deep learning based natural language processing. iIEEE Comput. Intell. Mag. 13, 3 (2018), 55–75.Google ScholarCross Ref
- [65] . 2008.
Manipulating the quota in weighted voting games . In AAAI, Vol. 8. 215–220.Google ScholarDigital Library
Index Terms
- Local Belief Dynamics in Network Knowledge Bases
Recommendations
A belief revision framework for revising epistemic states with partial epistemic states
Belief revision performs belief change on an agent's beliefs when new evidence (either of the form of a propositional formula or of the form of a total pre-order on a set of interpretations) is received. Jeffrey's rule is commonly used for revising ...
Revocable Belief Revision
Krister Segerberg proposed irrevocable belief revision, to be contrasted with standard belief revision, in a setting wherein belief of propositional formulas is modelled explicitly. This suggests that in standard belief revision is revocable: one should ...
General Belief Revision
In artificial intelligence, a key question concerns how an agent may rationally revise its beliefs in light of new information. The standard (AGM) approach to belief revision assumes that the underlying logic contains classical propositional logic. This ...
Comments