Socia Media and Digital Volunteering in Disaster Management @ DSEM 2017
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This document discusses using social media and digital volunteering in disaster management. It outlines how crowdsourcing can be used to extract insights from social media data during disasters through tasks like event detection, content labeling, and quality assessment. However, it notes challenges like biases in the data. The document proposes moving beyond individual insights to develop a "big picture" understanding of disasters. It also suggests moving beyond basic crowd processing to more advanced participatory mining with volunteers. Combining authoritative data with social media and integrating human and machine intelligence are presented as promising approaches.
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Socia Media and Digital Volunteering in Disaster Management @ DSEM 2017
- 1. Social Media and Digital Volunteering in Disaster Management Carlos Castillo Universitat Pompeu Fabra Data Science for Emergency Management Workshop Co-Located with IEEE Big Data. Boston, MA, US, Dec. 2018. www.bigcrisisdata.org
- 2. Research topics in this space just my opinion 2 Crowded Event detection Isolated messages Hashtag-based collection Crowd labeling Isolated quality aspects “Over-the-fence” transfer Having some elbow room “Big picture” inferences Conversational streams Adaptive information filtering Participatory mining Holistic content quality Interdisciplinary research Carlos Castillo — www.bigcrisisdata.org
- 3. From "actionable insights" to the "big picture" 3Carlos Castillo — www.bigcrisisdata.org
- 4. "Big picture" questions Understanding general parameters of a disaster How many people were affected? (Number of displaced, injured, dead) How much will the disaster cost? (Damaged or destroyed infrastructure) How many resource units need to be mobilized? (Beds in emergency shelters, tons of food, water) What is the extent of the affected area? 4Carlos Castillo — www.bigcrisisdata.org
- 5. Problematic for supervised learning 5 Source: USGS Large-scale disasters are fortunately rare Impact distributions are skewed Dependency between disaster impacts and social media response is not trivial (concave response conjecture) What we learn in one place and time may not transfer to another Carlos Castillo — www.bigcrisisdata.org
- 6. Let's not forget we have other types of data (traditional, or "authoritative" sources) 6 Auth Social Auth Social Predictions Model Modeling and data fusion done by user Data fusion done by user
- 7. Example 1: Floods Top: Sensor data Bottom: Tweets about floods Flood activity in Twitter is more pronounced in populated areas close to the Elbe river 7[De Albuquerque et al. 2015] Auth Soci al
- 8. Example 2: Landslides 8 Authoritative (e.g., rainfall) and non-authoritative (e.g., Twitter, Youtube, Facebook) information can be combined. Keyword search: landslide, mudslide [Musaev et al. 2014;2015] Auth Social Predictions Model
- 9. 9 Auth Social Predictions Model Could we do this? [Musaev et al. 2014;2015]
- 10. 10 Auth Social Predictions Model 1 Model 2 What about modeling an intermediate variable? Partial prediction
- 11. Example with intermediate variable: rain and floods Based on geocoded tweets (<2% of total) filtered to select those having keywords related to rain ("chuva" in Portuguese). Used to estimate rainfall (supervised setting), which is then fed into the flood risk prediction model 11[Restrepo-Estrada et al. 2018] Auth Social Predictions Model 1 Model 2Partial prediction
- 12. Challenge: biases [Olteanu et al. 2016] 12Carlos Castillo — www.bigcrisisdata.org
- 13. General biases Population biases (e.g., Twitter users more affluent, white, young than general population) Behavioral biases (decisions to perform actions or not depends on things we don't observe) Content biases / linking biases (e.g., choices to post or not post certain types of content) Temporal variations Redundancy which can be helpful or not 13Carlos Castillo — www.bigcrisisdata.org
- 14. Issues at the data source/platform People's behavior in a platform are affected, among others, by ... Functional biases What the platform allows or encourages (e.g., longer tweets beneficial for our work?) Normative biases Written and unwritten norms of what is acceptable behavior 14Carlos Castillo — www.bigcrisisdata.org
- 15. Data acquisition and processing introduce biases The way in which we acquire, query, and filter data introduces biases We often collect data in an adversarial manner Our choice of query (e.g., by geo, by keywords) changes the data we obtain The way in which we clean, enrich, and aggregate data introduces biases Our annotation processes are not perfect 15Carlos Castillo — www.bigcrisisdata.org
- 16. We often classify when we want to quantify Instead of using a text quantification framework, we do it indirectly by running a classifier and then selecting a threshold 16Tweet sentiment: From classification to quantification [Gao and Sebastiani 2015] W F S If shade represents classification accuracy (certain to uncertain): are there more messages about water, food, or shelter?
- 17. Interpretation challenges Mixing qualitative and quantitative methods Usage of abstract metrics vs domain-specific metrics E.g., “dollars saved, lives preserved, time conserved, effort reduced, quality of living increased” [Wagstaff 2012] More on the limits of social data: http://www.aolteanu.com/SocialDataLimitsTutorial/ 17
- 18. Hybrid systems 18Carlos Castillo — www.bigcrisisdata.org
- 19. Typical crowdsourcing tasks 19 Credit: MicroMappers / SBTF / QCRI
- 20. Typical crowdsourcing tasks (cont.) 20[Ofli et al. 2016] Credit: MicroMappers / UAViators / QCRI
- 22. 22 Volunteers are fast! They take just 15 seconds to label an item, so 5 of them can do about one item every 3 seconds. Carlos Castillo — www.bigcrisisdata.org
- 23. 23 In 3 seconds, more items arrive than are tagged For instance: 1M per day = 11.6 per second: ≈ 35 new items in 3 seconds.
- 24. 24 Space is not the problem. Time is. These items waited for too long: information expires! Carlos Castillo — www.bigcrisisdata.org
- 25. 25 Hundreds of people working 24/7 would be needed to keep up with these high item arrival rates Maybe AI can help?
- 26. 26 AI can learn from humans and tag 30-40 items per second! [Imran et al. 2013]
- 27. 27 Copies waiting to be tagged by humans AI creates copies of items that were hard for it to tag [Imran et al. 2013]
- 28. 28 AI learns from the more ambiguous cases and improves over time [Imran et al. 2013]
- 29. A general framework: Expert-Machine-Crowd The crowd annotates a few items and provides training data The machine annotates most items The expert designs and validates the annotations 29[Imran et al. 2013]
- 31. Could volunteers do more? 31Carlos Castillo — www.bigcrisisdata.org
- 32. What motivates them? 32 Values: "I feel it is important to help others'' Understanding: "It lets me learn through direct, hands-on experience'' Enhancement: "It makes me feel better about myself'' Career: "It may help me get my foot in the door at a place where I want to work'' Social: "People I know share an interest in community service'' Protective: "It is a good escape from my own troubles'' [Clary and Snyder 1999]
- 33. Example motivations 33 Coleman et al. [2009]: ● Altruism ● Professional or personal interest ● Intellectual stimulation ● Social reward ● Enhanced personal reputation Capelo et al. [2012]: ● Ideology ● Personal satisfaction ● Community ● Humanitarian values ● Desire to apply and improve technical knowledge Starbird et al. [2012]: ● Social capital: new friends and/or stronger relationships ● Symbolic capital: reputation ● Self-improvement: learning new skills ● Benevolence: to benefit others ● Entertainment Some elements in common with Free/Libre Open Source Software groups [Benkler, 2006] Some of these corroborated by various surveys
- 34. In this setting, they could do much more Create and refine categorization schemes Detect and describe outliers Generate hypotheses Suggest high-level interpretations … participatory mining instead of mere crowd processing 34Carlos Castillo — www.bigcrisisdata.org
- 35. Conclusions 35Carlos Castillo — www.bigcrisisdata.org
- 36. Conclusions 36 Two interesting directions: From "actionable insights" to the "big picture" From crowd processing to participatory mining Two powerful combinations: Authoritative and non-authoritative data Human and machine intelligence Carlos Castillo — www.bigcrisisdata.org
- 37. Thank you! @chatox @bigcrisisdata Free chapters available: http://bigcrisisdata.org/ 37