pith. sign in

arxiv: 1907.07725 · v1 · pith:QZ7IT5U3new · submitted 2019-07-17 · 💻 cs.SI · cs.CY

Cross-Media Usage of Social Big Data for Emergency Services and Volunteer Communities: Approaches, Development and Challenges of Multi-Platform Social Media Services

Marc-Andr\'e Kaufhold , Christian Reuter , Thomas Ludwig This is my paper

Pith reviewed 2026-05-24 19:43 UTC · model grok-4.3

classification 💻 cs.SI cs.CY
keywords cross-platform social mediaemergency servicessocial big datavolunteer communitiesinformation qualityAPI designdisaster managementdata gathering
0
0 comments X

The pith

A cross-platform Social Media API gathers and manages data from multiple platforms for emergency services and volunteers.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents the design of a cross-platform Social Media API integrated and evaluated across multiple emergency scenarios. It addresses how social media data, spread across platforms, can support emergency services and volunteer communities when existing tools face technical or business restrictions. The work identifies four core challenges based on practical development and theoretical findings. A reader would care because fragmented social media data can supply timely situational information during crises if collection and use can be unified effectively.

Core claim

The design, integration, and evaluation of a cross-platform Social Media API within multiple emergency scenarios leads to the identification of core challenges in (1) cross-platform gathering and data management, (2) trustability and information quality, (3) tailorability and adjustable data operations, and (4) queries, performance, and technical development.

What carries the argument

The cross-platform Social Media API, which unifies gathering, analysis, and distribution of social media data across platforms for emergency actors.

Load-bearing premise

The limitations of existing systems are primarily due to technical or business-oriented restrictions that a new cross-platform API can address.

What would settle it

A direct comparison showing that single-platform tools already meet emergency needs as effectively as the cross-platform API without resolving the listed challenges.

read the original abstract

The use of social media is ubiquitous and nowadays well-established in our everyday life, but increasingly also before, during or after emergencies. The produced data is spread across several types of social media and can be used by different actors, such as emergency services or volunteer communities. There are already systems available that support the process of gathering, analysing and distributing information through social media. However, dependent on the goal of analysis, the analysis methods and available systems are limited based on technical or business-oriented restrictions. This paper presents the design of a cross-platform Social Media API, which was integrated and evaluated within multiple emergency scenarios. Based on the lessons learned, we outline the core challenges from the practical development and theoretical findings, focusing (1) cross-platform gathering and data management, (2) trustability and information quality, (3) tailorability and adjustable data operations, and (4) queries, performance, and technical development.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript presents the design of a cross-platform Social Media API intended to support emergency services and volunteer communities in gathering, analyzing, and distributing social media data across platforms. The API was integrated and evaluated in multiple emergency scenarios, from which the authors derive four categories of core challenges based on practical development experience: (1) cross-platform gathering and data management, (2) trustability and information quality, (3) tailorability and adjustable data operations, and (4) queries, performance, and technical development.

Significance. If the described development work, integration steps, and scenario evaluations are documented with concrete implementation details, performance data, and traceable challenge derivations, the paper would offer a useful practitioner-oriented case study in crisis informatics. Such work can help surface recurring engineering and data-quality issues that arise when moving beyond single-platform tools, providing a reference point for future multi-platform systems even if the specific API is not adopted.

major comments (1)
  1. [Abstract] Abstract and § on evaluation (inferred from structure): the central claim that challenges were identified from integration and evaluation in multiple emergency scenarios is load-bearing, yet the manuscript provides no scenario descriptions, evaluation metrics, quantitative outcomes, or excerpts from the API implementation. Without these, it is not possible to verify how the four challenge categories were extracted or to assess their generality.
minor comments (1)
  1. [Abstract] Abstract: the phrasing 'focusing (1) cross-platform...' is grammatically awkward and should be revised to 'focusing on (1) ...' for readability.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for highlighting the need for stronger empirical grounding. We agree that the manuscript's central claim regarding derivation of challenges from scenario evaluations requires additional concrete details to be verifiable. We will revise to address this.

read point-by-point responses

  1. Referee: [Abstract] Abstract and § on evaluation (inferred from structure): the central claim that challenges were identified from integration and evaluation in multiple emergency scenarios is load-bearing, yet the manuscript provides no scenario descriptions, evaluation metrics, quantitative outcomes, or excerpts from the API implementation. Without these, it is not possible to verify how the four challenge categories were extracted or to assess their generality.

    Authors: We accept this critique. The current version emphasizes the four challenge categories derived from practical experience but does not include the requested supporting material. In revision we will add: (1) brief descriptions of the emergency scenarios and integration contexts, (2) any available evaluation metrics or qualitative outcomes, and (3) short API excerpts or pseudocode illustrating the technical issues that led to each challenge category. This will make the derivation traceable and allow assessment of generality without altering the paper's practitioner-oriented focus. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper is a descriptive systems-development report that presents the design, integration, and evaluation of a cross-platform Social Media API across emergency scenarios and then enumerates four categories of challenges derived from that work. No equations, fitted parameters, predictions, or mathematical derivations appear in the abstract or stated claims. The central claim reduces to the factual report of having performed the described development work and extracted the listed challenges; this is self-contained against external benchmarks because it does not invoke any self-citation chain, uniqueness theorem, or ansatz that would require external verification to stand. No load-bearing step reduces by construction to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that social media data can usefully support emergency services and that cross-platform integration overcomes single-platform limitations, with no free parameters or invented entities.

axioms (1)
  • domain assumption Social media data is useful for emergency services and volunteer communities before, during, or after emergencies.
    Opening premise of the abstract that motivates the entire work.

pith-pipeline@v0.9.0 · 5704 in / 1052 out tokens · 18986 ms · 2026-05-24T19:43:05.734462+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

52 extracted references · 52 canonical work pages

  1. [1]

    I ♥ FB: A Q-Methodology Analysis of Why People ‘Like’’ Facebook,’

    T. Robinson, C. Callahan, K. Boyle, E. Rivera, and J. K. Cho, 2017. “I ♥ FB: A Q-Methodology Analysis of Why People ‘Like’’ Facebook,’” Int. J. Virtual Communities Soc. Netw., vol. 9, no. 2, pp. 46–61

    work page 2017

  2. [2]

    Collective Behaviour, Social Media, and Disasters: a Systematic Literature Review,

    K. Eismann, O. Posegga, and K. Fischbach, 2016. “Collective Behaviour, Social Media, and Disasters: a Systematic Literature Review, ” in European Conference on Information Systems (ECIS)

    work page 2016

  3. [3]

    Sensemaking in Social Media C risis Communication – A Case Study on the Brussels Bombings in 2016,

    M. Mirbabaie and E. Zapatka, 2017. “Sensemaking in Social Media C risis Communication – A Case Study on the Brussels Bombings in 2016,” in Twenty-Fifth European Conference on Information Systems (ECIS) , pp. 2169– 2186

    work page 2017

  4. [4]

    Fifteen Years of Social Media in Emergencies: A Retrospective R eview and Future Directions for Crisis Informatics,

    C. Reuter and M. -A. Kaufhold, 2018. “Fifteen Years of Social Media in Emergencies: A Retrospective R eview and Future Directions for Crisis Informatics,” J. Contingencies Cris. Manag., vol. 26, no. 1, pp. 41–57

    work page 2018

  5. [5]

    From Conspiracies to Insults: A Case Study of Radicalisation in Social Media Disco urse,

    T. Riebe, K. Pätsch, M. -A. Kaufhold, and C. Reuter, 2018. “From Conspiracies to Insults: A Case Study of Radicalisation in Social Media Disco urse,” in Mensch und Computer 2018: Workshopband, pp. 595–603

    work page 2018

  6. [6]

    ‘On the Ground’ in Sidi Bouzid: Investigating Social Media Use during the Tunisian Revolution,

    V. Wulf, K. Misaki, M. Atam, D. Randall, and M. Rohde, 2013. “‘On the Ground’ in Sidi Bouzid: Investigating Social Media Use during the Tunisian Revolution,” in Proceedings of the Conferenc e on Computer Supported Cooperative Work (CSCW), pp. 1409–1418

    work page 2013

  7. [7]

    The Role of Modern Technology in Arab Spring,

    W. A. Alhindi, M. Talha, and G. Bin Sulong, 2012. “The Role of Modern Technology in Arab Spring,” Arch. Des Sci., vol. 65, no. 8, pp. 101–1661

    work page 2012

  8. [8]

    A. L. Hughes and L. Palen, 2014. Social Media in Emergency Management: Academic Perspective. FEMA in Higher Education Program, 2014

    work page 2014

  9. [9]

    Kenski, B

    K. Kenski, B. Hardy, and K. H. Jamieson, 2010. The Obama Victory: How Media, Money, and Message Shaped the 2008 Election. Oxford University Press, 2010

    work page 2010

  10. [10]

    Conceptualizing Big Social Data,

    E. Olshannikova, T. Olsson, J. Huhtamäki, and H. Kärkkäinen, 2017. “Conceptualizing Big Social Data,” J. Big Data, vol. 4, no. 1, pp. 1–19

    work page 2017

  11. [11]

    Microblogging During Two Natural Hazards Events: What Twitter May Contribute to Situational Awareness,

    S. Vieweg, A. L. Hughes, K. Starbird, and L. Palen, 2010. “Microblogging During Two Natural Hazards Events: What Twitter May Contribute to Situational Awareness, ” in Proceedings of the Conference on Human Factors in Computing Systems (CHI), pp. 1079–1088

    work page 2010

  12. [12]

    112.social: Design and Evaluatio n of a Mobile Crisis App for Bidirectional Communication between Emergency Services and Citizens,

    M.-A. Kaufhold, N. Rupp, C. Reuter, C. Amelunxen, and M. Cristaldi, 2018. “112.social: Design and Evaluatio n of a Mobile Crisis App for Bidirectional Communication between Emergency Services and Citizens, ” in European Conference on Information Systems (ECIS)

    work page 2018

  13. [13]

    The Self -Organization of Digital Volunteers across Social Med ia: The Case of the 2013 European Floods in Germany,

    M.-A. Kaufhold and C. Reuter, 2016. “The Self -Organization of Digital Volunteers across Social Med ia: The Case of the 2013 European Floods in Germany,” J. Homel. Secur. Emerg. Manag., vol. 13, no. 1, pp. 137–166

    work page 2016

  14. [14]

    Social media analytics – Challenges in topic discovery, data collection, a nd data preparation,

    S. Stieglitz, M. Mirbabaie, B. Ross, and C. Neuberger, 2018. “Social media analytics – Challenges in topic discovery, data collection, a nd data preparation,” Int. J. Inf. Manage., vol. 39, pp. 156–168

    work page 2018

  15. [15]

    Online Public Communications by Police & Fire Services during the 2012 Hurricane Sandy,

    A. L. Hughes, L. A. St. Denis, L. Palen, and K. M. Anderson, 2014. “Online Public Communications by Police & Fire Services during the 2012 Hurricane Sandy,” in Proceedings of the Confer ence on Human Factors in Computing Systems (CHI), pp. 1505–1514

    work page 2014

  16. [16]

    XHELP: Design of a Cross-Platform Social-Media Application to Support Volunteer Moderators in Disasters,

    C. Reuter, T. Ludwig, M.-A. Kaufhold, and V. Pipek, 2015. “XHELP: Design of a Cross-Platform Social-Media Application to Support Volunteer Moderators in Disasters,” in Proceedings of the Conference on Human Factors in Computing Systems (CHI), pp. 4093–4102

    work page 2015

  17. [17]

    Technical Limitations for Designing Applications for Social Media,

    C. Reuter and S. Scholl, 2014. “Technical Limitations for Designing Applications for Social Media, ” in Mensch & Computer 2014: Workshopband , pp. 131–140

    work page 2014

  18. [18]

    Software Innovations to Support the Use of Social Media by Emergency Managers,

    L. Plotnick and S. R . Hiltz, 2018. “Software Innovations to Support the Use of Social Media by Emergency Managers, ” Int. J. Hum. Comput. Interact. , vol. 34, no. 4, pp. 367–381

    work page 2018

  19. [19]

    Mitigating Information Overload in Social Media during Conflicts and Crises: Design and Evaluation of a Cross-Platform Alerting System,

    M.-A. Kaufhold, N. Rupp, C. Reuter, and M. Habdank, 2019. “Mitigating Information Overload in Social Media during Conflicts and Crises: Design and Evaluation of a Cross-Platform Alerting System,” Behav. Inf. Technol

    work page 2019

  20. [20]

    Avoiding chaotic use of social media before, during, an d after emergencies: Design and evaluation of citizens ’ guidelines,

    M.-A. Kaufhold, A. Gizikis, C. Reuter, M. Habdank, and M. Grinko, 2019. “Avoiding chaotic use of social media before, during, an d after emergencies: Design and evaluation of citizens ’ guidelines,” J. Contingencies Cris. Manag. , pp. 1–16

    work page 2019

  21. [21]

    Big Data in a Crisis? Creating Social Media Datase ts for Emergency Management Research,

    C. Reuter, T. Ludwig, C. Kotthaus, M. -A. Kaufhold, E. von Radziewski, and V. Pipek, 2016. “Big Data in a Crisis? Creating Social Media Datase ts for Emergency Management Research, ” i-com J. Interact. Media , vol. 15, no. 3, pp. 249–264

    work page 2016

  22. [22]

    Global data center IP traffic from 2013 to 2020

    Statista, 2017. “Global data center IP traffic from 2013 to 2020.” [Online]. Available: https://www.statista.com/statistics/227246/global -data-center-ip- traffic-development-forecast/

    work page 2017

  23. [23]

    Trending: The Promises and the Challenges of Big Social Data,

    L. Manovich, 2011. “Trending: The Promises and the Challenges of Big Social Data,” Debates Digit. Humanit., pp. 1–10

    work page 2011

  24. [24]

    Social Analytics - Gartner IT Glossary

    Gartner, 2016. “Social Analytics - Gartner IT Glossary. ” [Online]. Available: https://www.gartner.com/it-glossary/social-analytics

    work page 2016

  25. [25]

    Users of the world, unite! The challenges and opportunities of Social Media,

    A. M. Kaplan and M. Haenlein, 2010. “Users of the world, unite! The challenges and opportunities of Social Media, ” Bus. Horiz., vol. 53, no. 1, pp. 59–68

    work page 2010

  26. [26]

    Social media analytics: a survey of techniques, tools and platforms,

    B. Batrinca and P. C. Treleaven, 2014. “Social media analytics: a survey of techniques, tools and platforms,” AI Soc., vol. 30, no. 1, pp. 89–116

    work page 2014

  27. [27]

    Social Media Analytics: An Interdisciplin ary Approach and Its Implications for Information Systems,

    S. Stieglitz, L. Dang -Xuan, A. Bruns, and C. Neuberger, 2014. “Social Media Analytics: An Interdisciplin ary Approach and Its Implications for Information Systems,” Bus. Inf. Syst. Eng., vol. 6, no. 2, pp. 89–96

    work page 2014

  28. [28]

    A Survey of Data Mining Techniques for Social Network Analysis,

    M. Zuber, 2014. “A Survey of Data Mining Techniques for Social Network Analysis,” Int. J. Res. Comput. Eng. Electron., vol. 3, no. 6, pp. 1–8

    work page 2014

  29. [29]

    Mining social media with social theories: a survey,

    J. Tang, Y. Chang, and H. Liu, 2014. “Mining social media with social theories: a survey,” ACM SIGKDD Explor. Newsl., vol. 15, no. 2, pp. 20–29

    work page 2014

  30. [30]

    The Emergence of Online Widescale Interaction in Unexpected Events : A ssistance , Alliance & Retreat,

    L. Palen and S. Vieweg, 2008. “The Emergence of Online Widescale Interaction in Unexpected Events : A ssistance , Alliance & Retreat, ” 2008 ACM Conf. Comput. Support. Coop. Work, pp. 117–126

    work page 2008

  31. [31]

    EvenTweet: Online Localized Event Detection from Twitter,

    H. Abdelhaq, C. Sengstock, and M. Gertz, 2013. “EvenTweet: Online Localized Event Detection from Twitter, ” Proc. VLDB Endow. , vol. 6, no. 12, pp. 1326– 1329

    work page 2013

  32. [32]

    Simple supervised document geolocation with geodesic grids,

    B. P. Wing and J. Baldridge, 2011. “Simple supervised document geolocation with geodesic grids, ” Proc. 49th Annu. Meet. Assoc. Comput. Linguist. Hum. Lang. Technol. - Vol. 1, pp. 955–964

    work page 2011

  33. [33]

    Erhebungsmethoden und Tools,

    S. Römer, 2015. “Erhebungsmethoden und Tools, ” in Social Medie Kompass 2015 / 20162, Bundesverband Digitale Wirtschaft (BVDW) e.V., Ed. Düsseldorf, 2015, pp. 14–16. Development and Challenges of Cross-Platform Social Media APIs arXiv.org e-Print archive, July, 2019

    work page 2015

  34. [34]

    Design Challenges / Solutions for Environments Supporting the Analysis of Social Media Data in Crisis Informatics Research,

    K. M. Anderson, A. A. Aydin, M. Barrenechea, A. Cardenas, M. Hakeem, and S. Jambi, 2015. “Design Challenges / Solutions for Environments Supporting the Analysis of Social Media Data in Crisis Informatics Research, ” in 2015 48th Hawaii International Conference on System Sciences, pp. 163–172

    work page 2015

  35. [35]

    Y. Mass, H. Roitman, A. Raviv, E. Tzoannos, E. Schinas, and S. Papadopoulos,

    work page

  36. [36]

    SocialSensor: Crawler for social and web resources (D4.1)

    “SocialSensor: Crawler for social and web resources (D4.1).”

    work page

  37. [37]

    EmerGent Deliverable 5.1: Identification of Social Network Providers and API Design

    T. Ludwig and C. Reuter, 2014. “EmerGent Deliverable 5.1: Identification of Social Network Providers and API Design.”

    work page 2014

  38. [38]

    Activity Streams 2.0,

    World Wide Web Consortium, 2016. “Activity Streams 2.0,” W3C Recommendation. [Online]. Ava ilable: https://www.w3.org/TR/activitystreams-core/

    work page 2016

  39. [39]

    A performance comparison of SQL and NoSQL databases,

    Y. Li and S. Manoharan, 2013. “A performance comparison of SQL and NoSQL databases,” IEEE Pacific RIM Conf. Commun. Comput. Signal Process. - Proc., pp. 15–19

    work page 2013

  40. [40]

    Social-QAS: Tailorable Quality Assessment Service for Social Media Content,

    C. Reuter, T. Ludwig, M. Ritzkati s, and V. Pipek, 2015. “Social-QAS: Tailorable Quality Assessment Service for Social Media Content, ” in Proceedings of the International Symposium on End-User Development (IS-EUD), pp. 156–170

    work page 2015

  41. [41]

    CrowdMonitor: Mobile Crowd Sensing for Assessing Physical and Digital Activities of Citizens during Emergencies,

    T. Ludwig, C. Reuter, T. Siebigteroth, and V. Pipek, 2015 . “CrowdMonitor: Mobile Crowd Sensing for Assessing Physical and Digital Activities of Citizens during Emergencies, ” in Proceedings of the Conference on Human Factors in Computing Systems (CHI), pp. 4083–4092

    work page 2015

  42. [42]

    Semi-Automatic Alerts and Notifications for Emergency Services based on Cross -Platform Social Media Data –Evaluation of a Prototype,

    C. Reuter, C. Amelunxen, and M. Moi, 2016 . “Semi-Automatic Alerts and Notifications for Emergency Services based on Cross -Platform Social Media Data –Evaluation of a Prototype, ” in Informatik 2016: von Menschen für Menschen

    work page 2016

  43. [43]

    The usability engineering life cycle,

    J. Nielsen, 1992. “The usability engineering life cycle, ” Computer (Long. Beach. Calif)., vol. 25, no. 3, pp. 12–22

    work page 1992

  44. [44]

    A. L. Strauss and J. Corbin, 1998. Basics of qualitative research: Techniques and procedures for developing grounded theory. Sage Publications, 1998

    work page 1998

  45. [45]

    Twidale, D

    M. Twidale, D. Randall, and R. Bentley, 1994. Situated evaluation for cooperative systems. Lancester, UK, 1994

    work page 1994

  46. [46]

    Social Haystack: Dynamic Quality Assessment of Citizen -Generated Content during Emergencies,

    T. Ludwig, C. Reuter, and V. Pipek, 2015. “Social Haystack: Dynamic Quality Assessment of Citizen -Generated Content during Emergencies, ” Trans. Hum. Comput. Interact., vol. 22, no. 4, pp. 17:1-17:27

    work page 2015

  47. [47]

    ANTLR: ANother Tool for Language Recognition

    ANTLR, 2017. “ANTLR: ANother Tool for Language Recognition. ” [Online]. Available: http://www.antlr.org/. [Accessed: 25-Nov-2017]

    work page 2017

  48. [48]

    Social Media in Crisis Management: An Evaluation and Analysis of Crisis Informatics Research,

    C. Reuter, A. L. Hughes, and M. -A. Kaufhold, 2018. “Social Media in Crisis Management: An Evaluation and Analysis of Crisis Informatics Research, ” Int. J. Human-Computer Interact., vol. 34, no. 4, pp. 280–294

    work page 2018

  49. [49]

    Anonymized Reference for Blind Review: Tool IV,

    D. Anonymized, 2016. “Anonymized Reference for Blind Review: Tool IV, ” Journal

    work page 2016

  50. [50]

    Community detection in social media: Performance and application considerations,

    S. Papadopoulos, Y. Kompatsiaris, A. Vakali, and P. Spyri donos, 2012. “Community detection in social media: Performance and application considerations,” Data Min. Knowl. Discov., vol. 24, no. 3, pp. 515–554

    work page 2012

  51. [51]

    Imran, C

    M. Imran, C. Castillo, F. Diaz, and S. Vieweg, 2015. A Processing Social Media Messages in Mass Emer gency: A Survey , vol. 47, no. 4. New York, NY: ACM, 2015

    work page 2015

  52. [52]

    Digitalisierung und Zivile Sicherheit: Zivilgesellschaftliche und betriebliche Kontinuität in Katastrophenlagen (KontiKat),

    C. Reuter et al. , 2017. “Digitalisierung und Zivile Sicherheit: Zivilgesellschaftliche und betriebliche Kontinuität in Katastrophenlagen (KontiKat),” in Sicherheit (DIAGONAL Jahrgang 38) , G. Hoch, H. Schröteler von Brandt, V. Stein, and A. Schwarz, Eds. Göttingen: Vandenhoeck & Ruprecht, 2017, pp. 207–224

    work page 2017