Data-Driven Computational Social Science: A Survey

Toole, 2015, Tracking employment shocks using mobile phone data, J. R. Soc. Interface, 12, 10.1098/rsif.2015.0185 Liu, 2018, Artificial intelligence in the 21st century, IEEE Access, 6, 34403, 10.1109/ACCESS.2018.2819688 Liu, 2018, A survey of scholarly data visualization, IEEE Access, 6, 19205, 10.1109/ACCESS.2018.2815030 Bedru, 2020, Big networks: a survey, Comput. Sci. Rev., 37, 10.1016/j.cosrev.2020.100247 Xia, 2019, A survey of measures for network motifs, IEEE Access, 7, 106576, 10.1109/ACCESS.2019.2926752 Lazer, 2009, Life in the network: the coming age of computational social science, Science, 323, 721, 10.1126/science.1167742 Cioffi-Revilla, 2010, Computational social science, Wiley Interdiscip. Rev.: Comput. Stat., 2, 259, 10.1002/wics.95 Cioffi Revilla, 2014 Guo, 2016, Mobigroup: enabling lifecycle support to social activity organization and suggestion with mobile crowd sensing, IEEE Trans. Human-Mach. Syst., 46, 390, 10.1109/THMS.2015.2503290 Ozdikis, 2016, Evidential estimation of event locations in microblogs using the Dempster–Shafer theory, Inf. Process. Manag., 52, 1227, 10.1016/j.ipm.2016.06.001 Christakis, 2009 Park, 2016, Women are warmer but no less assertive than men: gender and language on Facebook, PLoS ONE, 11, 10.1371/journal.pone.0155885 Digman, 1990, Personality structure: emergence of the five-factor model, Annu. Rev. Psychol., 41, 417, 10.1146/annurev.ps.41.020190.002221 de Montjoye, 2013, Predicting personality using novel mobile phone-based metrics, 48 Zhao, 2013, Emergence of scaling in human-interest dynamics, Sci. Rep., 3, 10.1038/srep03472 Zheng Ren, 2019, Api: an index for quantifying a scholar's academic potential, IEEE Access, 7, 178675, 10.1109/ACCESS.2019.2958649 Bonacich, 1987, Power and centrality: a family of measures, Am. J. Sociol., 92, 1170, 10.1086/228631 Zhang, 2019, Judging a book by its cover: the effect of facial perception on centrality in social networks Sun, 2017, Identifying influential nodes in complex networks based on weighted formal concept analysis, IEEE Access, 5, 3777, 10.1109/ACCESS.2017.2679038 Neshati, 2017, On dynamicity of expert finding in community question answering, Inf. Process. Manag., 53, 1026, 10.1016/j.ipm.2017.04.002 Weng, 2010, Twitterrank: finding topic-sensitive influential twitterers, 261 Subbian, 2016, Mining influencers using information flows in social streams, ACM Trans. Knowl. Discov. Data, 10, 26, 10.1145/2815625 Hong, 2017, User vitality ranking and prediction in social networking service: a dynamic network perspective, IEEE Trans. Knowl. Data Eng., 29, 1343, 10.1109/TKDE.2017.2672749 Ortigosa, 2014, Sentiment analysis in Facebook and its application to e-learning, Comput. Hum. Behav., 31, 527, 10.1016/j.chb.2013.05.024 Hu, 2013, Unsupervised sentiment analysis with emotional signals, 607 Xia, 2020, Ranking station importance with human mobility patterns using subway network datasets, IEEE Trans. Intell. Transp. Syst., 21, 2840, 10.1109/TITS.2019.2920962 Xia, 2018, Modeling and analysis of large-scale urban mobility for green transportation, IEEE Trans. Ind. Inform., 14, 1469, 10.1109/TII.2017.2785383 Gonzalez, 2008, Understanding individual human mobility patterns, Nature, 453, 779, 10.1038/nature06958 Song, 2010, Modelling the scaling properties of human mobility, Nat. Phys., 6, 818, 10.1038/nphys1760 Barabási, 2005, The origin of bursts and heavy tails in human dynamics, Nature, 435, 207, 10.1038/nature03459 Farrahi, 2010, Probabilistic mining of socio-geographic routines from mobile phone data, IEEE J. Sel. Top. Signal Process., 4, 746, 10.1109/JSTSP.2010.2049513 Tsou, 2014, A community of curious souls: an analysis of commenting behavior on ted talks videos, PLoS ONE, 9, 10.1371/journal.pone.0093609 Smith, 2012, How does brand-related user-generated content differ across youtube, Facebook, and Twitter?, J. Interact. Mark., 26, 102, 10.1016/j.intmar.2012.01.002 Kuebler, 2013, When overweight is the normal weight: an examination of obesity using a social media Internet database, PLoS ONE, 8, 10.1371/journal.pone.0073479 Escobedo, 2017, Shine-l: sensing health and family behavior routines for Latino families, 2560 Golder, 2011, Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures, Science, 333, 1878, 10.1126/science.1202775 Yang, 2016, Life satisfaction and the pursuit of happiness on Twitter, PLoS ONE, 11, 10.1371/journal.pone.0150881 Song, 2010, Limits of predictability in human mobility, Science, 327, 1018, 10.1126/science.1177170 Zhang, 2016, Gmove: group-level mobility modeling using geo-tagged social media, 1305 Sinatra, 2014, Entropy and the predictability of online life, Entropy, 16, 543, 10.3390/e16010543 Xu, 2020, Multivariate relations aggregation learning in social networks Liu, 2020, Web of scholars: a scholar knowledge graph Steurer, 2013, Acquaintance or partner? Predicting partnership in online and location-based social networks, 372 Backstrom, 2014, Romantic partnerships and the dispersion of social ties: a network analysis of relationship status on Facebook, 831 Oloritun, 2013, Identifying close friendships in a sensed social network, Proc., Soc. Behav. Sci., 79, 18, 10.1016/j.sbspro.2013.05.054 Ju, 2017, A novel relationship strength model for online social networks, Multimed. Tools Appl., 76, 17577, 10.1007/s11042-017-4408-4 Tang, 2012, Inferring social ties across heterogenous networks, 743 Choi, 2013, Mining social relationship types in an organization using communication patterns, 295 Wang, 2017, Shifu: deep learning based advisor-advisee relationship mining in scholarly big data, 303 Liu, 2019, Shifu2: a network representation learning based model for advisor-advisee relationship mining, IEEE Trans. Knowl. Data Eng., 10.1109/TKDE.2019.2946825 Rotabi, 2017, Detecting strong ties using network motifs, 983 Min, 2013, Mining smartphone data to classify life-facets of social relationships, 285 Yang, 2011, Like like alike: joint friendship and interest propagation in social networks, 537 Parimi, 2011, Predicting friendship links in social networks using a topic modeling approach, 75 Zhang, 2013, Learning latent friendship propagation networks with interest awareness for link prediction, 63 Sadilek, 2012, Finding your friends and following them to where you are, 723 Cranshaw, 2010, Bridging the gap between physical location and online social networks, 119 Crandall, 2010, Inferring social ties from geographic coincidences, Proc. Natl. Acad. Sci., 107, 22436, 10.1073/pnas.1006155107 Hou, 2019, Prediction methods and applications in the science of science: a survey, Comput. Sci. Rev., 34, 10.1016/j.cosrev.2019.100197 Steurer, 2013, Predicting interactions in online social networks: an experiment in second life, 5 Nasim, 2016, Investigating link inference in partially observable networks: friendship ties and interaction, IEEE Trans. Comput. Soc. Syst., 3, 113, 10.1109/TCSS.2016.2618998 Yu, 2013, Understanding social relationship evolution by using real-world sensing data, World Wide Web, 16, 749, 10.1007/s11280-012-0189-x Chen, 2013, Friendship prediction on social network users, 379 Tang, 2016, Recommendations in signed social networks, 31 Bischoff, 2012, We love rock'n'roll: analyzing and predicting friendship links in last. fm, 47 Castilho, 2014, Working with friends: unveiling working affinity features from Facebook data He, 2015, Trust prediction with trust antecedent framework regularization, 177 Tang, 2013, Exploiting homophily effect for trust prediction, 53 Yu, 2014, Predicting co-author relationship in medical co-authorship networks, PLoS ONE, 9, 10.1371/journal.pone.0101214 Yu, 2017, Team recognition in big scholarly data: exploring collaboration intensity Xia, 2014, Mvcwalker: random walk based most valuable collaborators recommendation exploiting academic factors, IEEE Trans. Emerg. Top. Comput., 2, 364, 10.1109/TETC.2014.2356505 Sun, 2012, When will it happen?: relationship prediction in heterogeneous information networks, 663 Meeder, 2011, We know who you followed last summer: inferring social link creation times in Twitter, 517 Vitak, 2014, Facebook makes the heart grow fonder: relationship maintenance strategies among geographically dispersed and communication-restricted connections, 842 Dong, 2011, Modeling the co-evolution of behaviors and social relationships using mobile phone data, 134 Xia, 2016, Pis: a multi-dimensional routing protocol for socially-aware networking, IEEE Trans. Mob. Comput., 15, 2825, 10.1109/TMC.2016.2517649 Xia, 2014, Exploiting social relationship to enable efficient replica allocation in ad-hoc social networks, IEEE Trans. Parallel Distrib. Syst., 25, 3167, 10.1109/TPDS.2013.2295805 Cattuto, 2010, Dynamics of person-to-person interactions from distributed rfid sensor networks, PLoS ONE, 5, 10.1371/journal.pone.0011596 Xia Yu, 2019, Academic team formulation based on Liebig's barrel: discovery of anticask effect, IEEE Trans. Comput. Soc. Syst., 6, 1083, 10.1109/TCSS.2019.2913460 Yu, 2019, Science of scientific team science: a survey, Comput. Sci. Rev., 31, 72, 10.1016/j.cosrev.2018.12.001 Browet, 2017, Incompatibility boundaries for properties of community partitions, IEEE Trans. Netw. Sci. Eng. Zachary, 1977, An information flow model for conflict and fission in small groups, J. Anthropol. Res., 33, 452, 10.1086/jar.33.4.3629752 Yang, 2015, Defining and evaluating network communities based on ground-truth, Knowl. Inf. Syst., 42, 181, 10.1007/s10115-013-0693-z Newman, 2012, Communities, modules and large-scale structure in networks, Nat. Phys., 8, 25, 10.1038/nphys2162 Kheirkhahzadeh, 2016, Efficient community detection of network flows for varying Markov times and bipartite networks, Phys. Rev. E, 93, 10.1103/PhysRevE.93.032309 Newman, 2004, Fast algorithm for detecting community structure in networks, Phys. Rev. E, 69, 10.1103/PhysRevE.69.066133 Newman, 2004, Detecting community structure in networks, Eur. Phys. J. B, 38, 321, 10.1140/epjb/e2004-00124-y Agarwal, 2008, Modularity-maximizing graph communities via mathematical programming, Eur. Phys. J. B, 66, 409, 10.1140/epjb/e2008-00425-1 Sales-Pardo, 2007, Extracting the hierarchical organization of complex systems, Proc. Natl. Acad. Sci., 104, 15224, 10.1073/pnas.0703740104 Rees, 2012, Overlapping community detection using a community optimized graph swarm, Soc. Netw. Anal. Min., 2, 405, 10.1007/s13278-012-0050-3 Girvan, 2002, Community structure in social and biological networks, Proc. Natl. Acad. Sci., 99, 7821, 10.1073/pnas.122653799 Chen, 2014, Community detection based on social interactions in a social network, J. Assoc. Inf. Sci. Technol., 65, 539, 10.1002/asi.22986 Amelio, 2014, Overlapping community discovery methods: a survey, 105 Newman, 2007, Mixture models and exploratory analysis in networks, Proc. Natl. Acad. Sci., 104, 9564, 10.1073/pnas.0610537104 Palla, 2005, Uncovering the overlapping community structure of complex networks in nature and society, Nature, 435, 814, 10.1038/nature03607 Wang, 2010, Discovering overlapping groups in social media, 569 Ahn, 2010, Link communities reveal multiscale complexity in networks, Nature, 466, 761, 10.1038/nature09182 Bródka, 2013, Ged: the method for group evolution discovery in social networks, Soc. Netw. Anal. Min., 3, 1, 10.1007/s13278-012-0058-8 Barabási, 2002, Evolution of the social network of scientific collaborations, Physica A, 311, 590, 10.1016/S0378-4371(02)00736-7 Palla, 2007, Quantifying social group evolution, Nature, 446, 664, 10.1038/nature05670 Sun, 2013, Social dynamics of science, Sci. Rep., 3 Turner, 1957 Miller, 2013 Núñez, 2015, Social choice theory Chevaleyre, 2007 Kanawati, 2014, Seed-centric approaches for community detection in complex networks, 197 Roos, 2013 Page, 1999 Nikolakopoulos, 2013, Ncdawarerank: a novel ranking method that exploits the decomposable structure of the web, 143 Xia, 2017, Socially-aware conference participant recommendation with personality traits, IEEE Syst. J., 11, 2255, 10.1109/JSYST.2014.2342375 Albanese, 2013, A multimedia recommender system, ACM Trans. Internet Technol., 13, 3, 10.1145/2532640 Otegi, 2014, Personalised pagerank for making recommendations in digital cultural heritage collections, 49 Schall, 2013, Measuring contextual partner importance in scientific collaboration networks, J. Informetr., 7, 730, 10.1016/j.joi.2013.05.003 Ackerman, 2014, The authorship dilemma: alphabetical or contribution?, 1487 Popescu, 2013, Group recommender systems as a voting problem, 412 Nowak, 1992, Evolutionary games and spatial chaos, Nature, 359, 826, 10.1038/359826a0 Hauert, 2004, Spatial structure often inhibits the evolution of cooperation in the snowdrift game, Nature, 428, 643, 10.1038/nature02360 Ichinose, 2017, Invasion of cooperation in scale-free networks: accumulated versus average payoffs, Artif. Life, 23, 25, 10.1162/ARTL_a_00220 Xu, 2016, Understanding cooperative behavior in structurally disordered populations, Eur. Phys. J. B, 89, 1, 10.1140/epjb/e2016-60826-y Vilone, 2012, Social and strategic imitation: the way to consensus, Sci. Rep., 2, 10.1038/srep00686 Croson, 1998 Queller, 2016, Kin selection and its discontents, Philos. Sci., 83, 861, 10.1086/687870 Khan, 2017, A survey on scholarly data: from big data perspective, Inf. Process. Manag., 53, 923, 10.1016/j.ipm.2017.03.006 Kong, 2019, Academic social networks: modeling, analysis, mining and applications, J. Netw. Comput. Appl., 132, 86, 10.1016/j.jnca.2019.01.029 Wang, 2017, From triadic closure to conference closure: the role of academic conferences in promoting scientific collaborations, Scientometrics, 113, 177, 10.1007/s11192-017-2468-x Newman, 2001, The structure of scientific collaboration networks, Proc. Natl. Acad. Sci., 98, 404, 10.1073/pnas.98.2.404 EJ, 2004, Coauthorship networks and patterns of scientific collaboration, Proc. Natl. Acad. Sci., 101, 5200 Xia, 2016, Bibliographic analysis of nature based on Twitter and Facebook altmetrics data, PLoS ONE, 11, 10.1371/journal.pone.0165997 Bales, 2014, Associating co-authorship patterns with publications in high-impact journals, J. Biomed. Inform., 52, 311, 10.1016/j.jbi.2014.07.015 Ozel, 2014, Co-authorship pair distribution patterns by gender, Scientometrics, 98, 703, 10.1007/s11192-013-1145-y Schall, 2014, A multi-criteria ranking framework for partner selection in scientific collaboration environments, Decis. Support Syst., 59, 1, 10.1016/j.dss.2013.10.001 Xia, 2016, Scientific article recommendation: exploiting common author relations and historical preferences, IEEE Trans. Big Data, 2, 101, 10.1109/TBDATA.2016.2555318 Kong, 2020, The gene of scientific success, ACM Trans. Knowl. Discov. Data, 14, 10.1145/3385530 Anderson, 1991 Balcan, 2009, Multiscale mobility networks and the spatial spreading of infectious diseases, Proc. Natl. Acad. Sci., 106, 21484, 10.1073/pnas.0906910106 Balcan, 2010, Modeling the spatial spread of infectious diseases: the global epidemic and mobility computational model, J. Comput. Sci., 1, 132, 10.1016/j.jocs.2010.07.002 Tizzoni, 2014, On the use of human mobility proxies for modeling epidemics, PLoS Comput. Biol., 10, 10.1371/journal.pcbi.1003716 Dodds, 2004, Universal behavior in a generalized model of contagion, Phys. Rev. Lett., 92, 10.1103/PhysRevLett.92.218701 Bozorgi, 2016, Incim: a community-based algorithm for influence maximization problem under the linear threshold model, Inf. Process. Manag., 52, 1188, 10.1016/j.ipm.2016.05.006 Goel, 2015, The structural virality of online diffusion, Manag. Sci., 62, 180, 10.1287/mnsc.2015.2158 Gui, 2014, Modeling topic diffusion in multi-relational bibliographic information networks, 649 Tang, 2013, Confluence: conformity influence in large social networks, 347 Myers, 2012, Information diffusion and external influence in networks, 33 Centola, 2010, The spread of behavior in an online social network experiment, Science, 329, 1194, 10.1126/science.1185231 Granovetter, 1978, Threshold models of collective behavior, Am. J. Sociol., 83, 1420, 10.1086/226707 Kramer, 2014, Experimental evidence of massive-scale emotional contagion through social networks, Proc. Natl. Acad. Sci., 111, 8788, 10.1073/pnas.1320040111 Miller, 2017, Mathematical models of sir disease spread with combined non-sexual and sexual transmission routes, Infect. Dis. Model., 2, 35 Kong, 2016, Urban traffic congestion estimation and prediction based on floating car trajectory data, Future Gener. Comput. Syst., 61, 97, 10.1016/j.future.2015.11.013 Zhong, 2016, Variability in regularity: mining temporal mobility patterns in London, Singapore and Beijing using smart-card data, PLoS ONE, 11, 10.1371/journal.pone.0149222 Gallotti, 2016, A stochastic model of randomly accelerated walkers for human mobility, Nat. Commun., 7, 10.1038/ncomms12600 Kong, 2017, Time-location-relationship combined service recommendation based on taxi trajectory data, IEEE Trans. Ind. Inform., 13, 1202, 10.1109/TII.2017.2684163 Ratti, 2011, The social nexus, Sci. Am., 305, 42, 10.1038/scientificamerican0911-42 D'Andrea, 2017, Detection of traffic congestion and incidents from gps trace analysis, Expert Syst. Appl., 73, 43, 10.1016/j.eswa.2016.12.018 Zheng, 2010, Geolife: a collaborative social networking service among user, location and trajectory, IEEE Data Eng. Bull., 33, 32 Miluzzo, 2008, Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application, 337 Yang, 2017, Indigenization of urban mobility, Physica A, Stat. Mech. Appl., 469, 232, 10.1016/j.physa.2016.11.101 Beiró, 2016, Predicting human mobility through the assimilation of social media traces into mobility models, EPJ Data Sci., 5, 10.1140/epjds/s13688-016-0092-2 Wang, 2016, Towards felicitous decision making: an overview on challenges and trends of big data, Inf. Sci., 367, 747, 10.1016/j.ins.2016.07.007 Piñeiro-Chousa, 2017, Influence of social media over the stock market, Psychol. Mark., 34, 101, 10.1002/mar.20976 Li, 2016, A tensor-based information framework for predicting the stock market, ACM Trans. Inf. Syst., 34, 11, 10.1145/2838731 Salehan, 2016, Predicting the performance of online consumer reviews: a sentiment mining approach to big data analytics, Decis. Support Syst., 81, 30, 10.1016/j.dss.2015.10.006 Beauchamp, 2017, Predicting and interpolating state-level polls using Twitter textual data, Am. J. Polit. Sci., 61, 490, 10.1111/ajps.12274 Goel, 2010, Predicting consumer behavior with web search, Proc. Natl. Acad. Sci., 107, 17486, 10.1073/pnas.1005962107 Lu, 2013, Approaching the limit of predictability in human mobility, Sci. Rep., 3, 10.1038/srep02923 Chittaranjan, 2011, Who's who with big-five: analyzing and classifying personality traits with smartphones, 29 Liu, 2019, Data mining and information retrieval in the 21st century: a bibliographic review, Comput. Sci. Rev., 34, 10.1016/j.cosrev.2019.100193 Liu, 2019, Two decades of information systems: a bibliometric review, Scientometrics, 118, 617, 10.1007/s11192-018-2974-5 Zheng, 2014, Urban computing: concepts, methodologies, and applications, ACM Trans. Intell. Syst. Technol., 5, 38, 10.1145/2629592 Gorinova, 2016, Predicting gaming related properties from Twitter accounts, 4357 Li, 2017, Information fusion in a multi-source incomplete information system based on information entropy, Entropy, 10.3390/e19110570 Liu, 2019, A general multi-source data fusion framework Bin, 2020, Multi-source urban data fusion for property value assessment: a case study in Philadelphia, Neurocomputing, 10.1016/j.neucom.2020.05.013 Raffaele, 2016, Multi-sensor fusion in body sensor networks: state-of-the-art and research challenges, Inf. Fusion Xu, 2017, Multi-source data fusion study in scientometrics, Scientometrics, 10.1007/s11192-017-2290-5 Huang, 2020, Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion, Simul. Model. Pract. Theory, 10.1016/j.simpat.2019.101981 Zhou, 2019, Information fusion for multi-source material data: progress and challenges, Appl. Sci. Hasan, 2013, Understanding urban human activity and mobility patterns using large-scale location-based data from online social media, 6 Mu, 2019, A review of deep learning research, KSII Trans. Int. Inf. Syst. Singh, 2013, Predicting spending behavior using socio-mobile features, 174 Kosinski, 2013, Private traits and attributes are predictable from digital records of human behavior, Proc. Natl. Acad. Sci., 110, 5802, 10.1073/pnas.1218772110 Noulas, 2012, Mining user mobility features for next place prediction in location-based services, vol. 12, 1038 Tang, 2012, Cross-domain collaboration recommendation, 1285 Wang, 2015, Friendbook: a semantic-based friend recommendation system for social networks, IEEE Trans. Mob. Comput., 14, 538, 10.1109/TMC.2014.2322373 Lee, 2017, Improving personalized recommendations using community membership information, Inf. Process. Manag., 53, 1201, 10.1016/j.ipm.2017.05.005 Xia, 2020, Random walks: a review of algorithms and applications, IEEE Trans. Emerg. Top. Comput. Intell., 4, 95, 10.1109/TETCI.2019.2952908 Wang, 2017, Exploiting social influence for context-aware event recommendation in event-based social networks Li Zhang, 2015, Who influenced you? Predicting retweet via social influence locality, ACM Trans. Knowl. Discov. Data, 9, 25, 10.1145/2700398 Lazer, 2014, The parable of Google flu: traps in big data analysis, Science, 343, 1203, 10.1126/science.1248506