Skip to main content
SpringerLink
Log in

Predicting charitable donations using social media

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

We study the relationship between chatter on social media and observed actions concerning charitable donation. One hypothesis is that a fraction of those who act will also tweet about it, implying a linear relation. However, if the contagion is present, we expect a superlinear scaling. We consider two scenarios: donations in response to a natural disaster, and regular donations. We empirically validate the model using two location-paired sets of social media and donation data, corresponding to the two scenarios. Results show a quadratic relation between chatter and action in emergency response case. In case of regular donations, we observe a near-linear relation. Additionally, regular donations can be explained by demographic factors, while for a disaster response social media is a much better predictor of action. A contagion model is used to predict the near-quadratic scaling for the disaster response case. This suggests that diffusion is present in emergency response case, while regular charity does not spread via social network. Understanding the scaling behavior that relates social media chatter to physical actions is an important step in estimating the extent of a response and for determining social media strategies to affect the response.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. http://www.iarpa.gov/index.php/research-programs/osi.

  2. The Contact and Excitation models are similar to the threshold and independent cascade models in influence propagation. The main difference is that in our process, the propagation stops after one step.

  3. For sparse graphs, \(q=O(\frac{1}{|V|})\) and for dense graphs, \(q=O(1).\)

  4. https://philanthropy.com/interactives/how-america-gives.

References

  • Adali S, Sisenda F, Magdon-Ismail M (2012) Actions speak as loud as words: predicting relationships from social behavior data. In: Proceedings of WWW, pp 689–698

  • Anshelevich E, Hate A, Magdon-Ismail M (2013) Seeding influential nodes in non-submodular models of information diffusion. In: Proceedings of AAMAS, pp 1305–1306

  • Arthur WB (1989) Competing technologies, increasing returns, and lock-in by historical events. Econ J 99(394):116–131

  • Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512

    Article  MathSciNet  MATH  Google Scholar 

  • Bekkers R, Wiepking P (2010) A literature review of empirical studies of philanthropy: eight mechanisms that drive charitable giving. Nonprofit Volunt Sect Q 40(5):924–973

    Article  Google Scholar 

  • Bikhchandani S, Hirshleifer D, Welch I (1998) Learning from the behavior of others: conformity, fads, and informational cascades. J Econ Perspect 12(3):151–170

    Article  Google Scholar 

  • Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM TIST 2(3):27.1–27.27

    Google Scholar 

  • Cheung CK, Chan CM (2000) Social-cognitive factors of donating money to charity, with special attention to an international relief organization. Eval Program Plan 23(2):241–253

    Article  Google Scholar 

  • Cook S, Conrad C, Fowlkes AL, Mohebbi MH (2011) Assessing Google flu trends performance in the United States during the 2009 Influenza virus A (H1N1) pandemic. PLoS ONE 6(8):e23610

  • Cosley D, Huttenlocher DP, Kleinberg JM, Lan X, Suri S (2010) Sequential influence models in social networks. AAAI Int Conf Weblogs Social Media 10:26–33

    Google Scholar 

  • Destro L, Holguín-Veras J (2011) Material convergence and its determinants. Transp Res Rec 2234(1):14–21

    Article  Google Scholar 

  • Domingos P, Richardson M (2001) Mining the network value of customers. In: Proceedings of KDD, pp 57–66

  • Doyle A, Katz G, Summers K, Ackermann C, Zavorin I, Lim Z, Muthiah S, Butler P, Self N, Zhao L, Lu CT, Khandpur RP, Fayed Y, Ramakrishnan N (2014) Forecasting significant societal events using the Embers streaming predictive analytics system. Big Data 2(4):185–195

    Article  Google Scholar 

  • Dredze M, Paul M, Bergsma S, Tran H (2013) Carmen: a Twitter geolocation system with applications to public health. In: HIAI Workshop (at AAAI’13), pp 20–24

  • Eubank S, Guclu H, Kumar VA, Marathe MV, Srinivasan A, Toroczkai Z, Wang N (2004) Modelling disease outbreaks in realistic urban social networks. Nature 429(6988):180–184

    Article  Google Scholar 

  • Hagar C (2013) Crisis informatics: perspectives of trust-is social media a mixed blessing? SLIS Stud Res J 2(2):2

    Google Scholar 

  • Holguín-Veras J, Jaller M, Van Wassenhove LN, Pérez N, Wachtendorf T (2012) On the unique features of post-disaster humanitarian logistics. J Oper Manag 30(7):494–506

    Article  Google Scholar 

  • Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of KDD, pp 137–146

  • Korolov R, Peabody J, Lavoie A, Das S, Magdon-Ismail M, Wallace W (2015) Actions are louder than words in social media. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015. ACM, pp 292–297

  • Kumar S, Morstatter F, Liu H (2014) Twitter data analytics. Springer, New York

    Book  Google Scholar 

  • Lee YK, Chang CT (2007) Who gives what to charity? Characteristics affecting donation behavior. Soc Behav Pers 35(9):1173–1180

    Article  Google Scholar 

  • Leskovec J, Singh A, Kleinberg J (2006) Patterns of influence in a recommendation network. In: Advances in KDD, pp 380–389

  • Li N, Chen G (2009) Analysis of a location-based social network. IEEE Int Conf Comput Sci Eng 4:263–270

    Google Scholar 

  • Morales A, Borondo J, Losada JC, Benito RM (2014) Efficiency of human activity on information spreading on twitter. Soc Netw 39:1–11

    Article  Google Scholar 

  • Muller A, Whiteman G (2009) Exploring the geography of corporate philanthropic disaster response: a study of Fortune Global 500 firms. J Bus Ethics 84(4):589–603

    Article  Google Scholar 

  • Oosterhof L, Heuvelman A, Peters O (2009) Donation to disaster relief campaigns: underlying social cognitive factors exposed. Eval Program Plan 32(2):148–157

    Article  Google Scholar 

  • Palen L, Liu SB (2007) Citizen communications in crisis: anticipating a future of ICT-supported public participation. In: Proceedings of SIGCHI, ACM, pp 727–736

  • Price DS (1976) A general theory of bibliometric and other cumulative advantage processes. JASIST 27(5):292–306

    Article  Google Scholar 

  • Radley A, Kennedy M (1995) Charitable giving by individuals: a study of attitudes and practice. Hum Relat 6:685–709

    Article  Google Scholar 

  • Romero DM, Galuba W, Asur S, Huberman BA (2011) Influence and passivity in social media. In: Proceedings of ECML/PKDD, pp 18–33

  • Santiago A, Benito R (2008) An extended formalism for preferential attachment in heterogeneous complex networks. EPL (Europhysics Letters) 82(5):58004

    Article  Google Scholar 

  • Scellato S, Mascolo C (2011) Measuring user activity on an online location-based social network. In: INFOCOM Workshops, IEEE, pp 918–923

  • Starbird K, Palen L (2011) Voluntweeters: self-organizing by digital volunteers in times of crisis. In: Proceedings of SIGCHI, pp 1071–1080

  • Starbird K, Palen L (2012) (How) will the revolution be retweeted? Information diffusion and the 2011 Egyptian uprising. In: Proceedings of ACM CSCW, ACM, pp 7–16

  • The Foundation Center (2014) Philanthropy and Hurricane Sandy. A report on the foundation and corporate response. http://foundationcenter.org/gainknowledge/research/pdf/sandy_philanthropy_2014.pdf. Accessed 6 Dec 2015

  • Torrey C, Burke M, Lee M, Dey A, Fussell S, Kiesler S (2007) Connected giving: ordinary people coordinating disaster relief on the Internet. In: Hawaii international conference on system sciences

  • Tyshchuk Y, Li H, Ji H, Wallace WA (2013) Evolution of communities on Twitter and the role of their leaders during emergencies. In: Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM2013), pp 727–733

  • Wang H, Hovy E, Dredze M (2015) The Hurricane Sandy Twitter Corpus. In: Workshops at the twenty-ninth AAAI conference on artificial intelligence, pp 20–24

  • Watts DJ (2002) A simple model of global cascades on random networks. PNAS 99(9):5766–5771

    Article  MathSciNet  MATH  Google Scholar 

  • Wu S, Hofman JM, Mason WA, Watts DJ (2011) Who says what to whom on Twitter. In: Proceedings of WWW, ACM, pp 705–714

Download references

Acknowledgments

This is an extended version of the paper entitled “Actions are Louder Than Words in Social Media” presented at the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2015). The authors gratefully acknowledge support from NSF Grant 1124827. This material is also based partially upon work sponsored by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053 and by Department of Homeland Security through the Command, Control, and Interoperability Center for Advanced Data Analysis Center of Excellence. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory, or the US Government.

Author information

Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Rensselaer Polytechnic Institute, 110 8th Street, CII 5015, Troy, NY, 12180-3590, USA

    Rostyslav Korolov

  2. Department of Computer Science and Engineering, Washington University in St. Louis, Bryan Hall, CB 1045, 1 Brookings Drive, Saint Louis, MO, 63130, USA

    Justin Peabody, Allen Lavoie & Sanmay Das

  3. Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY, 12180-3590, USA

    Malik Magdon-Ismail & William Wallace

Authors
  1. Rostyslav Korolov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Justin Peabody
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Allen Lavoie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sanmay Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Malik Magdon-Ismail
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. William Wallace
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rostyslav Korolov.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Korolov, R., Peabody, J., Lavoie, A. et al. Predicting charitable donations using social media. Soc. Netw. Anal. Min. 6, 31 (2016). https://doi.org/10.1007/s13278-016-0341-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-016-0341-1

Keywords

Search

Navigation