Tag Archives: analysis

Using Twitter to Map Blackouts During Hurricane Sandy

I recently caught up with Gilal Lotan during a hackathon in New York and was reminded of his good work during Sandy, the largest Atlantic hurricane on record. Amongst other analytics, Gilal created a dynamic map of tweets referring to power outages. “This begins on the evening October 28th as people mostly joke about the prospect of potentially losing power. As the storm evolves, the tone turns much more serious. The darker a region on the map, the more aggregate Tweets about power loss that were seen for that region.” The animated map is captured in the video below.

Hashtags played a key role in the reporting. The #NJpower hashtag, for example, was used to ‘help  keep track of the power situation throughout the state (1). As depicted in the tweet below, “users and news outlets used this hashtag to inform residents where power outages were reported and gave areas updates as to when they could expect their power to come back” (1). 

NJpower tweet

As Gilal notes, “The potential for mapping out this kind of information in realtime is huge. Think of generating these types of maps for different scenarios– power loss, flooding, strong winds, trees falling.” Indeed, colleagues at FEMA and ESRI had asked us to automatically extract references to gas leaks on Twitter in the immediate aftermath of the Category 5 Tornado in Oklahoma. One could also use a platform like GeoFeedia, which maps multiple types of social media reports based on keywords (i.e., not machine learning). But the vast majority of Twitter users do not geo-tag their tweets. In fact, only 2.7% of tweets are geotagged, according to this study. This explains why enlightened policies are also important for humanitarian technologies to work—like asking the public to temporally geo-tag their social media updates when these are relevant to disaster response.

While basing these observations on people’s Tweets might not always bring back valid results (someone may jokingly tweet about losing power),” Gilal argues that “the aggregate, especially when compared to the norm, can be a pretty powerful signal.” The key word here is norm. If an established baseline of geo-tagged tweets for the northeast were available, one would have a base-map of “normal” geo-referenced twitter activity. This would enable us to understand deviations from the norm. Such a base-map would thus place new tweets in temporal and geo-spatial context.

In sum, creating live maps of geo-tagged tweets is only a first step. Base-maps should be rapidly developed and overlaid with other datasets such as population and income distribution. Of course, these datasets are not always available acessing historical Twitter data can also be a challenge. The latter explains why Big Data Philanthropy for Disaster Response is so key.

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Crowdsourcing Crisis Information from Syria: Twitter Firehose vs API

Over 400 million tweets are posted every day. But accessing 100% of these tweets (say for disaster response purposes) requires access to Twitter’s “Firehose”. The latter, however, can be prohibitively expensive and also requires serious infrastructure to manage. This explains why many (all?) of us in the Crisis Computing & Humanitarian Technology space use Twitter’s “Streaming API” instead. But how representative are tweets sampled through the API vis-a-vis overall activity on Twitter? This is important question is posed and answered in this new study using Syria as a case study.

Tweets Syria

The analysis focused on “Tweets collected in the region around Syria during the period from December 14, 2011 to January 10, 2012.” The first dataset was collected using Firehose access while the second was sampled from the API. The tag clouds above (click to enlarge) displays the most frequent top terms found in each dataset. The hashtags and geoboxes used for the data collection are listed in the table below.

Syria List

The graph below shows the number of tweets collected between December 14th, 2011 and January 10th, 2012. This amounted 528,592 tweets from the API and 1,280,344 tweets from the Firehose. On average, the API captures 43.5% of tweets available on the Firehose. “One of the more interesting results in this dataset is that as the data in the Firehose spikes, the Streaming API coverage is reduced. One possible explanation for this phenomenon could be that due to the Western holidays observed at this time, activity on Twitter may have reduced causing the 1% threshold to go down.”

Syria Graph

The authors, Fred Morstatter, Jürgen Pfeffer, Huan Liu and Kathleen Carley, also carry out hashtag analysis using each dataset. “Here we see mixed results at small values of n [top hashtags], indicating that the Streaming data may not be good for finding the top hashtags. At larger values of n, we see that the Streaming API does a better job of estimating the top hashtags in the Firehose data.” In addition, the analysis reveals that the “Streaming API data does not consistently find the top hashtags, in some cases revealing reverse correlation with the Firehose data [...]. This could be indicative of a filtering process in Twitter’s Streaming API which causes a misrepresentation of top hashtags in the data.”

In terms of social network analysis, the the authors were able to show that “50% to 60% of the top 100 key-players [can be identified] when creating the networks based on one day of Streaming API data.” Aggregating more days’ worth of data “can increase the accuracy substantially. For network level measures, first in-depth analysis revealed interesting correlation between network centralization indexes and the proportion of data covered by the Streaming API.”

Finally, study also compares the geolocation of tweets. More specifically, the authors assess how the “geographic distribution of the geolocated tweets is affected by the sampling performed by the Streaming API. The number of geotagged tweets is low, with only 16,739 geotagged tweets in the Streaming data (3.17%) and 18,579 in the Firehose data (1.45%).” Still, the authors find that “despite the difference in tweets collected on the whole we get 90.10% coverage of geotagged tweets.”

In sum, the study finds that “the results of using the Streaming API depend strongly on the coverage and the type of analysis that the researcher wishes to perform. This leads to the next question concerning the estimation of how much data we actually get in a certain time period.” This is critical if researchers want to place their results into context and potentially apply statistical methods to account (and correct) for bias. The authors suggest that in some cases the Streaming API coverage can be estimated. In future research, they hope to “find methods to compensate for the biases in the Streaming API to provide a more accurate picture of Twitter activity to researchers.” In particularly they want to “determine whether the methodology presented here will yield similar results for Twitter data collected from other domains, such as natural, protest & elections.”

The authors will present their paper at this year’s International Conference on Weblogs and Social Media (ICWSM). So I look forward to meeting them there to discuss related research we are carrying out at QCRI.

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 See also:

Results: Analyzing 2 Million Disaster Tweets from Oklahoma Tornado

Thanks to the excellent work carried out by my colleagues Hemant Purohit and Professor Amit Sheth, we were able to collect 2.7 million tweets posted in the aftermath of the Category 4 Tornado that devastated Moore, Oklahoma. Hemant, who recently spent half-a-year with us at QCRI, kindly took the lead on carrying out some preliminary analysis of the disaster data. He sampled 2.1 million tweets posted during the first 48 hours for the analysis below.

oklahoma-tornado-20

About 7% of these tweets (~146,000 tweets) were related to donations of resources and services such as money, shelter, food, clothing, medical supplies and volunteer assistance. Many of the donations-related tweets were informative in nature, e.g.: “As President Obama said this morning, if you want to help the people of Moore, visit [link]”. Approximately 1.3% of the tweets (about 30,000 tweets) referred to the provision of financial assistance to the disaster-affected population. Just over 400 unique tweets sought non-monetary donations, such as “please help get the word out, we are accepting kid clothes to send to the lil angels in Oklahoma.Drop off.

Exactly 152 unique tweets related to offers of help were posted within the first 48 hours of the Tornado. The vast majority of these were asking how to get involved in helping others affected by the disaster. For example: “Anyone know how to get involved to help the tornado victims in Oklahoma??#tornado #oklahomacity” and “I want to donate to the Oklahoma cause shoes clothes even food if I can.” These two offers of help are actually automatically “matchable”, making the notion of a “Match.com” for disaster response a distinct possibility. Indeed, Hemant has been working with my team and I at QCRI to develop algorithms (classifiers) that not only identify relevant needs/offers from Twitter automatically but also suggests matches as a result.

Some readers may be suprised to learn that “only” several hundred unique tweets (out of 2+million) were related to needs/offers. The first point to keep in mind is that social media complements rather than replaces traditional information sources. All of us working in this space fully recognize that we are looking for the equivalent of needles in a haystack. But these “needles” may contain real-time, life-saving information. Second, a significant number of disaster tweets are retweets. This is not a negative, Twitter is particularly useful for rapid information dissemination during crises. Third, while there were “only” 152 unique tweets offering help, this still represents over 130 Twitter users who were actively seeking ways to help pro bono within 48 hours of the disaster. Plus, they are automatically identifiable and directly contactable. So these volunteers could also be recruited as digital humanitarian volunteers for MicroMappers, for example. Fourth, the number of Twitter users continues to skyrocket. In 2011, Twitter had 100 million monthly active users. This figure doubled in 2012. Fifth, as I’ve explained here, if disaster responders want to increase the number of relevant disaster tweets, they need to create demand for them. Enlightened leadership and policy is necessary. This brings me to point six: we were “only” able to collect ~2 million tweets but suspect that as many as 10 million were posted during the first 48 hours. So humanitarian organizations along with their partners need access to the Twitter Firehose. Hence my lobbying for Big Data Philanthropy.

Finally, needs/offers are hardly the only type of useful information available on Twitter during crises, which is why we developed several automatic classifiers to extract data on: caution and advice, infrastructure damage, casualties and injuries, missing people and eyewitness accounts. In the near future, when our AIDR platform is ready, colleagues from the American Red Cross, FEMA, UN, etc., will be able create their own classifiers on the fly to automatically collect information that is directly relevant to them and their relief operations. AIDR is spearheaded by QCRI colleague ChaTo and myself.

For now though, we simply emailed relevant geo-tagged and time-stamped data on needs/offers to colleagues at the American Red Cross who had requested this information. We also shared data related to gas leaks with colleagues at FEMA and ESRI, as per their request. The entire process was particularly insightful for Hemant and I, so we plan to follow up with these responders to learn how we can best support them again until AIDR becomes operational. In the meantime, check out the Twitris+ platform developed by Amit, Hemant and team at Kno.e.sis

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See also: Analysis of Multimedia Shared on Twitter After Tornado [Link

Analyzing Tweets Posted During Mumbai Terrorist Attacks

Over 1 million unique users posted more than 2.7 million tweets in just 3 days following the triple bomb blasts that struck Mumbai on July 13, 2011. Out of these, over 68,000 tweets were “original tweets” (in contrast to retweets) and related to the bombings. An analysis of these tweets yielded some interesting patterns. (Note that the Ushahidi Map of the bombings captured ~150 reports; more here).

One unique aspect of this study (PDF) is the methodology used to assess the quality of the Twitter dataset. The number of tweets per user was graphed in order to test for a power law distribution. The graph below shows the log distri-bution of the number of tweets per user. The straight lines suggests power law behavior. This finding is in line with previous research done on Twitter. So the authors conclude that the quality of the dataset is comparable to the quality of Twitter datasets used in other peer-reviewed studies.

I find this approach intriguing because Professor Michael Spagat, Dr. Ryan Woodard and I carried out related research on conflict data back in 2006. One fascinating research question that emerges from all this, and which could be applied to twitter datasets, is whether the slope of the power law says anything about the type of conflict/disaster being tweeted about, the expected number of casualties or even the propagation of rumors.  If you’re interested in pursuing this research question (and have worked with power laws before), please do get in touch. In the meantime, I challenge the authors’ suggestion that a power law distribution necessarily says anything about the quality or reliability of the underlying data. Using the casualty data from SyriaTracker (which is also used by USAID in their official crisis maps), my colleague Dr. Ryan Woodard showed that this dataset does not follow a power law distribution—even thought it is one of the most reliable on Syria.

Syria_PL

Moving on to the content analysis of the Mumbai blast tweets:  “The number of URLs and @-mentions in tweets increase during the time of the crisis in com-parison to what researchers have exhibited for normal circumstances.” The table below lists the top 10 URLs shared on Twitter. Inter-estingly, the link to a Google Spreadsheet was amongst the most shared resource. Created by Twitter user Nitin Sagar, the spreadsheet was used to “coordinate relief operation among people. Within hours hundreds of people registered on the sheet via Twitter. People asked for or off ered help on that spreadsheet for many hours.”

The analysis also reveals that “the number of tweets or updates by authority users (those with large number of followers) are very less, i.e., majority of content generated on Twitter during the crisis comes from non authority users.”  In addition, tweets generated by authority users have a high level of retweets. The results also indicate that “the number of tweets generated by people with large follower base (who are generally like government owned accounts, cele-brities, media companies) were very few. Thus, the majority of content generated at the time of crisis was from unknown users. It was also observed that, though the number of posts were less by users with large number of followers, these posts registered high numbers of retweets.”

Rumors related to the blasts also spread through Twitter. For example, rumors began to circulate about a fourth bomb going off. “Some tweets even speci fied locations of 4th blast as Lemington street, Colaba and Charni. Around 500+ tweets and retweets were posted about this.” False rumors about hospital blood banks needing donations were also propagated via Twitter. “They were initiated by a user, @KapoorChetan and around 2,000 tweets and retweets were made regarding this by Twitter users.” The authors of the study believe that such false rumors and can be prevented if credible sources like the mainstream media companies and the government post updates on social media more frequently.

I did a bit of research on this and found that NDTV did use their twitter feed (which has over half-a-million followers) to counter these rumors. For example, “RT @ndtv: Mumbai police: Don’t believe rumours of more bombs. False rumours being spread deliberately.” Journalist Sonal Kalra also acted to counter rumors: “RT @sonalkalra: BBMs about bombs found in Delhi are FALSE. Pls pls don’t spread rumours. #mumbaiblasts.”

In conclusion, the study considers the “privacy threats during the Twitter activity after the blasts. People openly tweeted their phone numbers on social media websites like Twitter, since at such moment of crisis people wished to reach out to help others. But, long after the crisis was over, such posts still remained publicly available on the Internet.” In addition, “people also openly posted their blood group, home address, etc. on Twitter to off er help to victims of the blasts.” The Ushahidi Map also includes personal information. These data privacy and security issues continue to pose major challenges vis-a-vis the use of social media for crisis response.

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See also: Did Terrorists Use Twitter to Increase Situational Awareness? [Link]

Big Data for Development: From Information to Knowledge Societies?

Unlike analog information, “digital information inherently leaves a trace that can be analyzed (in real-time or later on).” But the “crux of the ‘Big Data’ paradigm is actually not the increasingly large amount of data itself, but its analysis for intelligent decision-making (in this sense, the term ‘Big Data Analysis’ would actually be more fitting than the term ‘Big Data’ by itself).” Martin Hilbert describes this as the “natural next step in the evolution from the ‘Information Age’ & ‘Information Societies’ to ‘Knowledge Societies’ [...].”

Hilbert has just published this study on the prospects of Big Data for inter-national development. “From a macro-perspective, it is expected that Big Data informed decision-making will have a similar positive effect on efficiency and productivity as ICT have had during the recent decade.” Hilbert references a 2011 study that concluded the following: “firms that adopted Big Data Analysis have output and productivity that is 5–6 % higher than what would be expected given their other investments and information technology usage.” Can these efficiency gains be brought to the unruly world of international development?

To answer this question, Hilbert introduces the above conceptual framework to “systematically review literature and empirical evidence related to the pre-requisites, opportunities and threats of Big Data Analysis for international development.” Words, Locations, Nature and Behavior are types of data that are becoming increasingly available in large volumes.

“Analyzing comments, searches or online posts [i.e., Words] can produce nearly the same results for statistical inference as household surveys and polls.” For example, “the simple number of Google searches for the word ‘unemployment’ in the U.S. correlates very closely with actual unemployment data from the Bureau of Labor Statistics.” Hilbert argues that the tremendous volume of free textual data makes “the work and time-intensive need for statistical sampling seem almost obsolete.” But while the “large amount of data makes the sampling error irrelevant, this does not automatically make the sample representative.” 

The increasing availability of Location data (via GPS-enabled mobile phones or RFIDs) needs no further explanation. Nature refers to data on natural processes such as temperature and rainfall. Behavior denotes activities that can be captured through digital means, such as user-behavior in multiplayer online games or economic affairs, for example. But “studying digital traces might not automatically give us insights into offline dynamics. Besides these biases in the source, the data-cleaning process of unstructured Big Data frequently introduces additional subjectivity.”

The availability and analysis of Big Data is obviously limited in areas with scant access to tangible hardware infrastructure. This corresponds to the “Infra-structure” variable in Hilbert’s framework. “Generic Services” refers to the production, adoption and adaptation of software products, since these are a “key ingredient for a thriving Big Data environment.” In addition, the exploitation of Big Data also requires “data-savvy managers and analysts and deep analytical talent, as well as capabilities in machine learning and computer science.” This corresponds to “Capacities and Knowledge Skills” in the framework.

The third and final side of the framework represents the types of policies that are necessary to actualize the potential of Big Data for international develop-ment. These policies are divided into those that elicit a Positive Feedback Loops such as financial incentives and those that create regulations such as interoperability, that is, Negative Feedback Loops.

The added value of Big Data Analytics is also dependent on the availability of publicly accessible data, i.e., Open Data. Hilbert estimates that a quarter of US government data could be used for Big Data Analysis if it were made available to the public. There is a clear return on investment in opening up this data. On average, governments with “more than 500 publicly available databases on their open data online portals have 2.5 times the per capita income, and 1.5 times more perceived transparency than their counterparts with less than 500 public databases.” The direction of “causality” here is questionable, however.

Hilbert concludes with a warning. The Big Data paradigm “inevitably creates a new dimension of the digital divide: a divide in the capacity to place the analytic treatment of data at the forefront of informed decision-making. This divide does not only refer to the availability of information, but to intelligent decision-making and therefore to a divide in (data-based) knowledge.” While the advent of Big Data Analysis is certainly not a panacea,”in a world where we desperately need further insights into development dynamics, Big Data Analysis can be an important tool to contribute to our understanding of and improve our contributions to manifold development challenges.”

I am troubled by the study’s assumption that we live in a Newtonian world of decision-making in which for every action there is an automatic equal and opposite reaction. The fact of the matter is that the vast majority of development policies and decisions are not based on empirical evidence. Indeed, rigorous evidence-based policy-making and interventions are still very much the exception rather than the rule in international development. Why? “Account-ability is often the unhappy byproduct rather than desirable outcome of innovative analytics. Greater accountability makes people nervous” (Harvard 2013). Moreover, response is always political. But Big Data Analysis runs the risk de-politicize a problem. As Alex de Waal noted over 15 years ago, “one universal tendency stands out: technical solutions are promoted at the expense of political ones.” I hinted at this concern when I first blogged about the UN Global Pulse back in 2009.

In sum, James Scott (one of my heroes) puts it best in his latest book:

“Applying scientific laws and quantitative measurement to most social problems would, modernists believed, eliminate the sterile debates once the ‘facts’ were known. [...] There are, on this account, facts (usually numerical) that require no interpretation. Reliance on such facts should reduce the destructive play of narratives, sentiment, prejudices, habits, hyperbole and emotion generally in public life. [...] Both the passions and the interests would be replaced by neutral, technical judgment. [...] This aspiration was seen as a new ‘civilizing project.’ The reformist, cerebral Progressives in early twentieth-century American and, oddly enough, Lenin as well believed that objective scientific knowledge would allow the ‘administration of things’ to largely replace politics. Their gospel of efficiency, technical training and engineering solutions implied a world directed by a trained, rational, and professional managerial elite. [...].”

“Beneath this appearance, of course, cost-benefit analysis is deeply political. Its politics are buried deep in the techniques [...] how to measure it, in what scale to use, [...] in how observations are translated into numerical values, and in how these numerical values are used in decision making. While fending off charges of bias or favoritism, such techniques [...] succeed brilliantly in entrenching a political agenda at the level of procedures and conventions of calculation that is doubly opaque and inaccessible. [...] Charged with bias, the official can claim, with some truth, that ‘I am just cranking the handle” of a nonpolitical decision-making machine.”

See also:

  • Big Data for Development: Challenges and Opportunities [Link]
  • Beware the Big Errors of Big Data (by Nassim Taleb) [Link]
  • How to Build Resilience Through Big Data [Link]

Social Network Analysis for Digital Humanitarian Response

Monitoring social media for digital humanitarian response can be a massive undertaking. The sheer volume and velocity of tweets generated during a disaster makes real-time social media monitoring particularly challenging if not near impossible. However, two new studies argue that there is “a better way to track the spread of information on Twitter that is much more powerful.”

Twitter-Hadoop31

Manuel Garcia-Herranz and his team at the Autonomous University of Madrid in Spain use small groups of “highly connected Twitter users as ‘sensors’ to detect the emergence of new ideas. They point out that this works because highly co-nnected individuals are more likely to receive new ideas before ordinary users.” The test their hypothesis, the team studied 40 million Twitters users who “together totted up 1.5 billion follows’ and sent nearly half a billion tweets, including 67 million containing hashtags.”

They found that small groups of highly connected Twitter users detect “new hashtags about seven days earlier than the control group.  In fact, the lead time varied between nothing at all and as much as 20 days.” Manuel and his team thus argue that “there’s no point in crunching these huge data sets. You’re far better off picking a decent sensor group and watching them instead.” In other words, “your friends could act as an early warning system, not just for gossip, but for civil unrest and even outbreaks of disease.”

The second study, “Identifying and Characterizing User Communities on Twitter during Crisis Events,” (PDF) is authored by Aditi Gupta et al. Aditi and her co-lleagues analyzed three major crisis events (Hurricane Irene, Riots in England and Earthquake in Virginia) to “to identify the different user communities, and characterize them by the top central users.” Their findings are in line with those shared by the team in Madrid. “[T]he top users represent the topics and opinions of all the users in the community with 81% accuracy on an average.” In sum, “to understand a community, we need to monitor and analyze only these top users rather than all the users in a community.”

How could these findings be used to prioritize the monitoring of social media during disasters? See this blog post for more on the use of social network analysis (SNA) for humanitarian response.

Social Network Analysis of Tweets During Australia Floods

This study (PDF) analyzes the community of Twitter users who disseminated  information during the crisis caused by the Australian floods in 2010-2011. “In times of mass emergencies, a phenomenon known as collective behavior becomes apparent. It consists of socio-behaviors that include intensified information search and information contagion.” The purpose of the Australian floods analysis is to reveal interesting patterns and features of this online community using social network analysis (SNA).

The authors analyzed 7,500 flood-related tweets to understand which users did the tweeting and retweeting. This was done to create nodes and links for SNA, which was able to “identify influential members of the online communities that emerged during the Queensland, NSW and Victorian floods as well as identify important resources being referred to. The most active community was in Queensland, possibly induced by the fact that the floods were orders of mag-nitude greater than in NSW and Victoria.”

The analysis also confirmed “the active part taken by local authorities, namely Queensland Police, government officials and volunteers. On the other hand, there was not much activity from local authorities in the NSW and Victorian floods prompting for the greater use of social media by the authorities concerned. As far as the online resources suggested by users are concerned, no sensible conclusion can be drawn as important ones identified were more of a general nature rather than critical information. This might be comprehensible as it was past the impact stage in the Queensland floods and participation was at much lower levels in the NSW and Victorian floods.”

Social Network Analysis is an under-utilized methodology for the analysis of communication flows during humanitarian crises. Understanding the topology of a social network is key to information diffusion. Think of this as a virus infecting a network. If we want to “infect” a social network with important crisis information as quickly and fully as possible, understanding the network’ topology is a requirement as is, therefore, social network analysis.