The analysis of people’s comments in social platforms is a widely investigated topic because comments are the place where people show their spontaneity most clearly. In this article, we present a network-based data structure and a related approach to represent and manage the underlying semantics of a set of comments. Our approach is based on the extraction of text patterns that take into account not only the frequency, but also the utility of the analysed comments. Our data structure and approach are ‘multidimensional’ and ‘holistic’, in the sense that they can simultaneously handle content semantics from multiple perspectives. They are also easily extensible, because additional content semantics perspectives can be easily added to them. Furthermore, our approach is able to evaluate the semantic similarity of two sets of comments. In this article, we also illustrate the results of several tests we conducted on Reddit comments, even if our approach can be applied to any social platform. Finally, we provide an overview of some possible applications of this research.
AsurSHubermanBA. Predicting the future with social media. In: Proceedings of the international conference on web intelligence and intelligent agent technology (WI-IAT’10), vol. 1, Toronto, ON, Canada, 31 August–3 September 2010, pp. 492–499. New York: IEEE.
2.
BenderJLJimenez-MarroquinM-CJadadAR. Seeking support on Facebook: a content analysis of breast cancer groups. J Med Internet Res2011; 13(1): e16.
3.
SheltonAKSkalskiP. Blinded by the light: illuminating the dark side of social network use through content analysis. Comput Hum Behav2014; 33: 339–348.
4.
ChenXYuanYOrgunMA. Using Bayesian networks with hidden variables for identifying trustworthy users in social networks. J Inf Sci2020; 46(5): 600–615.
5.
FazzingaBFlescaSFurfaroFet al. Efficiently interpreting traces of low level events in business process logs. Inf Syst2018; 73: 1–24.
6.
FazzingaBFlescaSFurfaroFet al. RFID-data compression for supporting aggregate queries. ACM T Database Syst2013; 38(2): 1–45.
7.
BoczkowskiPJMatassiMMitchelsteinE. How young users deal with multiple platforms: the role of meaning-making in social media repertoires. J Comput-Mediat Comm2018; 23(5): 245–259.
8.
DubrofskyREWoodMM. Posting racism and sexism: authenticity, agency and self-reflexivity in social media. Commun Crit-Cult Stu2014; 11(3): 282–287.
9.
Van KoningsbruggenGMHartmannTEdenAet al. Spontaneous hedonic reactions to social media cues. Cyberpsychol Behav Soc Netw2017; 20(5): 334–340.
10.
DougnonRYFournier-VigerPNkambouR. Inferring user profiles in online social networks using a partial social graph. In: Proceedings of the Canadian conference on artificial intelligence, Halifax, NS, Canada, 2–5 June 2015, pp. 84–99. Cham: Springer.
11.
MisloveAViswanathBGummadiKPet al. You are who you know: inferring user profiles in online social networks. In: Proceedings of the third ACM international conference on web search and data mining (WSDM’10), New York, 4–6 February 2010, pp. 251–260. New York: ACM Press.
12.
CauteruccioFCorradiniETerracinaGet al. Investigating Reddit to detect subreddit and author stereotypes and to evaluate author assortativity. J Inf Sci. Epub ahead of print 20December2020. DOI: 10.1177/0165551520979869.
13.
Abu-SalihBWongthongthamPChanKYet al. CredSaT: credibility ranking of users in big social data incorporating semantic analysis and temporal factor. J Inf Sci2019; 45(2): 259–280.
14.
AhmadianSAfsharchiMMeghdadiM. An effective social recommendation method based on user reputation model and rating profile enhancement. J Inf Sci2019; 45(5): 607–642.
15.
MareeMKmailABBelkhatirM. Analysis and shortcomings of e-recruitment systems: towards a semantics-based approach addressing knowledge incompleteness and limited domain coverage. J Inf Sci2019; 45(6): 713–735.
16.
AgarwalRSrikantR. Fast algorithms for mining association rules. In: Proceedings of the international VLDB conference (VLDB’94), Santiago de Chile, Chile, 12–15 September 1994, pp. 487–499. Burlington, MA: Morgan Kaufmann.
17.
Fournier-VigerPLinJC-WKiranRUKohYSet al. A survey of sequential pattern mining. Data Sci Pattern Recognit2017; 1(1): 54–77.
18.
Fournier-VigerPLinJCWVoBet al. A survey of itemset mining. Wires Data Min Knowl Discov2017; 7(4): e1207.
19.
AggarwalCCBhuiyanMHasanMA. Frequent pattern mining algorithms: a survey. In: HanJAggarwalC (eds) Frequent pattern mining. Cham: Springer, 2014, pp. 19–64.
20.
MoosaviSAJalaliMMisaghianNet al. Community detection in social networks using user frequent pattern mining. Knowl Inf Syst2017; 51(1): 159–186.
PearsonK. Note on regression and inheritance in the case of two parents. P R Soc London1895; 58: 240–242.
23.
DjenouriYBelhadiAFournier-VigerPet al. Fast and effective cluster-based information retrieval using frequent closed itemsets. Inf Sci2018; 453: 154–167.
24.
BouraouiZCamacho-ColladosJSchockaertS. Inducing relational knowledge from BERT. In: Proceedings of the international conference on artificial intelligence (AAAI 2020), vol. 34, issue 5, New York, 7–12 February 2020, pp. 7456–7463. Menlo Park, CA: Association for the Advancement of Artificial Intelligence.
25.
BerlingerioMKoutraDEliassi-RadTet al. NetSimile: a scalable approach to size-independent network similarity. arXiv2012: 1209.2684, https://arxiv.org/pdf/1209.2684.pdf
26.
Fronzetti ColladonAGuardabascioBInnarellaR. Using social network and semantic analysis to analyze online travel forums and forecast tourism demand. Decis Support Syst2019; 123: 113075.
27.
KwonKHChris BangCEgnotoMet al. Social media rumors as improvised public opinion: semantic network analyses of twitter discourses during Korean saber rattling 2013. Asian J Commun2016; 26(3): 201–222.
28.
YooMLeeSHaT. Semantic network analysis for understanding user experiences of bipolar and depressive disorders on Reddit. Inform Process Manag2019; 56(4): 1565–1575.
29.
ChoiCLecyJD. A semantic network analysis of changes in North Korea’s economic policy. Governance2012; 25(4): 589–616.
30.
Baeza-YatesRRibeiro-NetoB. Modern information retrieval. Harlow: Addison Wesley Longman, 1999.
31.
TausczikYRPennebakerJW. The psychological meaning of words: LIWC and computerized text analysis methods. J Lang Soc Psychol2010; 29(1): 24–54.
32.
QinMJinDLeiKet al. Adaptive community detection incorporating topology and content in social networks. Knowl-Based Syst2018; 161: 342–356.
33.
LiuWYueKWuHet al. Markov-network based latent link analysis for community detection in social behavioral interactions. Appl Intell2018; 48(8): 2081–2096.
34.
ReihanianAFeizi-DerakhshiMRAghdasiHS. Overlapping community detection in rating-based social networks through analyzing topics, ratings and links. Pattern Recogn2018; 81: 370–387.
35.
ZhaoJWuJFengXet al. Information propagation in online social networks: a tie-strength perspective. Knowl Inf Syst2012; 32(3): 589–608.
36.
RadicioniTSquartiniTPavanEet al. Networked partisanship and framing: a socio-semantic network analysis of the Italian debate on migration. arXiv2021: 2103.04653, https://arxiv.org/pdf/2103.04653.pdf
37.
WuZCaoJWuJet al. Detecting genuine communities from large-scale social networks: a pattern-based method. Comput J2014; 57(9): 1343–1357.
38.
AdnanMAlhajjRRokneJG. Identifying social communities by frequent pattern mining. In: Proceedings of the international conference on information visualisation (IV’09), Barcelona, 15–17 July 2009, pp. 413–418. New York: IEEE.
39.
HuttoCJGilbertE. Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the international AAAI conference on weblogs and social media (ICWSM’14), Ann Arbor, MI, 1–4 June 2014, pp. 216–225. Menlo Park, CA: AAAI.
GanWLinCFournier-VigerPet al. A survey of utility-oriented pattern mining. IEEE T Knowl Data En2021; 33(4): 1306–1327.
42.
YaoHHamiltonHJGengL. A unified framework for utility-based measures for mining itemsets. In: Proceedings of the ACM SIGKDD workshop on utility-based data mining (UBDM’06), Philadelphia, PA, 20 August 2006, pp. 28–37. New York: ACM.
43.
GerberHUPafumG. Utility functions: from risk theory to finance. N Am Actuar J1998; 2(3): 74–91.
44.
HanJPeiJYinYet al. Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min Knowl Disc2004; 8(1): 53–87.
McPhersonMSmith-LovinLCookJM. Birds of a feather: homophily in social networks. Annu Rev Sociol2001; 27: 415–444.
50.
GaoXXiaoBTaoDet al. A survey of graph edit distance. Pattern Anal Appl2010; 13(1): 113–129.
51.
FernándezMValienteG. A graph distance metric combining maximum common subgraph and minimum common supergraph. Pattern Recogn Lett2001; 22(6–7): 753–758.
52.
PalopoliLSaccàDTerracinaGet al. Uniform techniques for deriving similarities of objects and subschemes in heterogeneous databases. IEEE T Knowl Data En2003; 15(2): 271–294.
53.
De MeoPQuattroneGTerracinaGet al. Integration of XML schemas at various ‘severity’ levels. Inf Syst2006; 31(6): 397–434.
54.
MillerAG. WordNet: a lexical database for English. Commun ACM1995; 38(11): 39–41.
MatiasJN. Going dark: social factors in collective action against platform operators in the Reddit blackout. In: Proceedings of the international conference on human factors in computing systems (ACM CHI 2016), San Jose, CA, 7–12 May 2016, pp. 1138–1151. New York: ACM.
57.
NarayananBKNirmalaM. Adult content filtering: restricting minor audience from accessing inappropriate Internet content. Educ Inf Technol2018; 23(6): 2719–2735.
58.
CorradiniENoceraAUrsinoDet al. Investigating the phenomenon of NSFW posts in Reddit. Inf Sci2021; 566: 140–164.
59.
BaumgartnerJZannettouSKeeganBet al. The pushshift Reddit dataset. In: Proceedings of the international AAAI conference on web and social media (ICWSM’20), vol. 14, Atlanta, GA, 8–11 June 2020, pp. 830–839. Menlo Park, CA: AAAI Press.
60.
ThoratPBGoudarRMBarveS. Survey on collaborative filtering, content-based filtering and hybrid recommendation system. Int J Comput Appl2015; 110(4): 31–36.
61.
KhatoonMBanuWA. A survey on community detection methods in social networks. Int J Educ Manag Eng2015; 5(1): 8.
62.
HanJKamberMPeiJ. Data mining: concepts and techniques. 3rd ed.Burlington, MA: Morgan Kaufmann, 2011.
