Tag Archives: media

What Happens When the Media Sends Drone Teams to Disasters?

Posted on October 26, 2016 | 1 comment

Media companies like AFP, CNN and others are increasingly capturing dramatic aerial footage following major disasters around the world. These companies can be part of the solution when it comes to adding value to humanitarian efforts on the ground. But they can also be a part of the problem.

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Media teams are increasingly showing up to disasters with small drones (UAVs) to document the damage. They’re at times the first with drones on the scene and thus able to quickly capture dramatic aerial footage of the devastation below. These media assets lead to more views and thus traffic on news websites, which increases the probability that more readers click on ads. Cue Noam Chomsky’s Manufacturing Consent: The Political Economy of the Mass Media, my favorite book whilst in high school.

Aerial footage can also increase situational awareness for disaster responders if that footage is geo-located. Labeling individual scenes in video footage with the name of the towns or villages being flown over would go a long way. This is what I asked one journalist to do in the aftermath of the Nepal Earthquake after he sent me dozens of his aerial videos. I also struck up an informal agreement with CNN to gain access to their raw aerial footage in future disasters. On a related note, I was pleased when my CNN contact expressed an interest in following the Humanitarian UAV Code of Conduct.

In an ideal world, there would be a network of professional drone journalists with established news agencies that humanitarian organizations could quickly contact for geo-tagged video footage after major disasters to improve their situational awareness. Perhaps the Professional Society of Drone Journalists (PSDJ) could be part of the solution. In any case, the network would either have its own Code of Conduct or follow the humanitarian one. Perhaps they could post their footage and pictures directly to the Humanitarian UAV Network (UAViators) Crisis Map. Either way, the media has long played an important role in humanitarian disasters, and their increasing use of drones makes them even more valuable partners to increase situational awareness.

The above scenario describes the ideal world. But the media can (and has) been part of the problem as well. “If it bleeds, it leads,” as the saying goes. Increased competition between media companies to be the first to capture dramatic aerial video that goes viral means that they may take shortcuts. They may not want to waste time getting formal approval from a country’s civil aviation authority. In Nepal after the earthquake, one leading company’s drone team was briefly detained by authorities for not getting official permission.

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Media companies may not care to engage with local communities. They may be on a tight deadline and thus dispense with getting community buy-in. They may not have the time to reassure traumatized communities about the robots flying overhead. Media companies may overlook or ignore potential data privacy repercussions of publishing their aerial videos online. They may also not venture out to isolated and rural areas, thus biasing the video footage towards easy-to-access locations.

So how do we in the humanitarian space make media drone teams part of the solution rather than part of the problem? How do we make them partners in these efforts? One way forward is to start a conversation with these media teams and their relevant networks. Perhaps we start with a few informal agreements and learn by doing. If anyone is interested in working with me on this and/or has any suggestions on how to make this happen, please do get in touch. Thanks!

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Posted in Crisis Mapping, Drones/UAVs, Robotics

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Mining Mainstream Media for Emergency Management 2.0

Posted on October 29, 2013 | 4 comments

There is so much attention (and hype) around the use of social media for emergency management (SMEM) that we often forget about mainstream media when it comes to next generation humanitarian technologies. The news media across the globe has become increasingly digital in recent years—and thus analyzable in real-time. Twitter added little value during the recent Pakistan Earthquake, for example. Instead, it was the Pakistani mainstream media that provided the immediate situational awareness necessary for a preliminary damage and needs assessment. This means that our humanitarian technologies need to ingest both social media and mainstream media feeds. 

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Now, this is hardly revolutionary. I used to work for a data mining company ten years ago that focused on analyzing Reuters Newswires in real-time using natural language processing (NLP). This was for a conflict early warning system we were developing. The added value of monitoring mainstream media for crisis mapping purposes has also been demonstrated repeatedly in recent years. In this study from 2008, I showed that a crisis map of Kenya was more complete when sources included mainstream media as well as user-generated content.

So why revisit mainstream media now? Simple: GDELT. The Global Data Event, Language and Tone dataset that my colleague Kalev Leetaru launched earlier this year. GDELT is the single largest public and global event-data catalog ever developed. Digital Humanitarians need no longer monitor mainstream media manually. We can simply develop a dedicated interface on top of GDELT to automatically extract situational awareness information for disaster response purposes. We’re already doing this with Twitter, so why not extend the approach to global digital mainstream media as well?

GDELT data is drawn from a “cross-section of all major international, national, regional, local, and hyper-local news sources, both print and broadcast, from nearly every corner of the globe, in both English and vernacular.” All identified events are automatically coded using the CAMEO coding framework (although Kalev has since added several dozen additional event-types). In short, GDELT codes all events by the actors involved, the type of event, location, time and other meta-data attributes. For example, actors include “Refugees,” “United Nations,” and “NGO”. Event-types include variables such as “Affect” which captures everything from refugees to displaced persons,  evacuations, etc. Humanitarian crises, aid, disasters, disaster relief, etc. are also included as an event-type. The “Provision of Humanitarian Aid” is another event-type, for example. GDELT data is currently updated every 24 hours, and Kalev has plans to provide hourly updates in the near future and ultimately 30-minute updates.

GDELT GKG

If this isn’t impressive enough, Kalev and colleagues have just launched the GDELT Global Knowledge Graph (GKG). “To sum up the GKG in a single sentence, it connects every person, organization, location, count, theme, news source, and event across the planet into a single massive network that captures what’s happening around the world, what its context is and who’s involved, and how the world is feeling about it, every single day.” The figure above (click to enlarge) is based on a subset of a single day of the GDELT Knowledge Graph, showing “how the cities of the world are connected to each other in a single day’s worth of news. A customized version of the GKG could perhaps prove useful for UN OCHA’s “Who Does What, Where” (3Ws) directory in the future. 

I’ve had long conversations with Kalev this month about leveraging GDELT for disaster response and he is very supportive of the idea. My hope is that we’ll be able to add a GDELT feed to MicroMappers next year. I’m also wondering whether we could eventually create a version of the AIDR platform that ingests GDELT data instead (or in addition to) Twitter. There is real potential here, which is why I’m excited that my colleagues at OCHA are exploring GDELT for humanitarian response. I’ll be meeting with them this week and next to explore ways to collaborate on making the most of GDELT for humanitarian response.

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Note: Mainstream media obviously includes television and radio as well. Some colleagues of mine in Austria are looking at triangulating television broadcasts with text-based media and social media for a European project.

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Truthiness as Probability: Moving Beyond the True or False Dichotomy when Verifying Social Media

Posted on March 10, 2012 | 25 comments

I asked the following question at the Berkman Center’s recent Symposium on Truthiness in Digital Media: “Should we think of truthiness in terms of probabili-ties rather than use a True or False dichotomy?” The wording here is important. The word “truthiness” already suggests a subjective fuzziness around the term. Expressing truthiness as probabilities provides more contextual information than does a binary true or false answer.

When we set out to design the SwiftRiver platform some three years ago, it was already clear to me then that the veracity of crowdsourced information ought to be scored in terms of probabilities. For example, what is the probability that the content of a Tweet referring to the Russian elections is actually true? Why use probabilities? Because it is particularly challenging to instantaneously verify crowdsourced information in the real-time social media world we live in.

There is a common tendency to assume that all unverified information is false until proven otherwise. This is too simplistic, however. We need a fuzzy logic approach to truthiness:

“In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree between 0 and 1. Fuzzy logic has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false.”

The majority of user-generated content is unverified at time of birth. (Does said data deserve the “original sin” of being labeled as false, unworthy, until prove otherwise? To digress further, unverified content could be said to have a distinct wave function that enables said data to be both true and false until observed. The act of observation starts the collapse of said wave function. To the astute observer, yes, I’m riffing off Shroedinger’s Cat, and was also pondering how to weave in Heisenberg’s uncertainty principle as an analogy; think of a piece of information characterized by a “probability cloud” of truthiness).

I believe the hard sciences have much to offer in this respect. Why don’t we have error margins for truthiness? Why not take a weather forecast approach to information truthiness in social media? What if we had a truthiness forecast understanding full well that weather forecasts are not always correct? The fact that a 70% chance of rain is forecasted doesn’t prevent us from acting and using that forecast to inform our decision-making. If we applied binary logic to weather forecasts, we’d be left with either a 100% chance of rain or 100% chance of sun. Such weather forecasts would be at best suspect if not wrong rather frequently.

In any case, instead of dismissing content generated in real-time because it is not immediately verifiable, we can draw on Information Forensics to begin assessing the potential validity of said content. Tactics from information forensics can help us create a score card of heuristics to express truthiness in terms of probabilities. (I call this advanced media literacy). There are indeed several factors that one can weigh, e.g., the identity of the messenger relaying the content, the source of the content, the wording of said content, the time of day the information was shared, the geographical proximity of the source to the event being reported, etc.

These weights need not be static as they are largely subjective and temporal; after all, truth is socially constructed and dynamic. So while a “wisdom of the crowds” approach alone may not always be well-suited to generating these weights, perhaps integrating the hunch of the expert coupled with machine learning algorithms (based on lessons learned in information forensics) could result more useful decision-support tools for truthiness forecasting (or rather “backcasting”).

In sum, thinking of truthiness strictly in terms of true and false prevents us from “complexifying” a scalar variable into a vector (a wave function), which in turn limits our ability to develop new intervention strategies. We need new conceptual frameworks to reflect the complexity and ambiguity of user-generated content:

 

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Posted in Big Data, Crowdsourcing, Information Forensics, Social Media

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Trails of Trustworthiness in Real-Time Streams

Posted on March 3, 2012 | 1 comment

Real-time information channels like Twitter, Facebook and Google have created cascades of information that are becoming increasingly challenging to navigate. “Smart-filters” alone are not the solution since they won’t necessarily help us determine the quality and trustworthiness of the information we receive. I’ve been studying this challenge ever since the idea behind SwiftRiver first emerged several years ago now.

I was thus thrilled to come across a short paper on “Trails of Trustworthiness in Real-Time Streams” which describes a start-up project that aims to provide users with a “system that can maintain trails of trustworthiness propagated through real-time information channels,” which will “enable its educated users to evaluate its provenance, its credibility and the independence of the multiple sources that may provide this information.” The authors, Panagiotis Metaxas and Eni Mustafaraj, kindly cite my paper on “Information Forensics” and also reference SwiftRiver in their conclusion.

The paper argues that studying the tactics that propagandists employ in real life can provide insights and even predict the tricks employed by Web spammers.

“To prove the strength of this relationship between propagandistic and spamming techniques, […] we show that one can, in fact, use anti-propagandistic techniques to discover Web spamming networks. In particular, we demonstrate that when starting from an initial untrustworthy site, backwards propagation of distrust (looking at the graph defined by links pointing to to an untrustworthy site) is a successful approach to finding clusters of spamming, untrustworthy sites. This approach was inspired by the social behavior associated with distrust: in society, recognition of an untrustworthy entity (person, institution, idea, etc) is reason to question the trust- worthiness of those who recommend it. Other entities that are found to strongly support untrustworthy entities become less trustworthy themselves. As in society, distrust is also propagated backwards on the Web graph.”

The authors document that today’s Web spammers are using increasingly sophisticated tricks.

“In cases where there are high stakes, Web spammers’ influence may have important consequences for a whole country. For example, in the 2006 Congressional elections, activists using Google bombs orchestrated an effort to game search engines so that they present information in the search results that was unfavorable to 50 targeted candidates. While this was an operation conducted in the open, spammers prefer to work in secrecy so that their actions are not revealed. So,  revealed and documented the first Twitter bomb, which tried to influence the Massachusetts special elections, show- ing how an Iowa-based political group, hiding its affiliation and profile, was able to serve misinformation a day before the election to more than 60,000 Twitter users that were follow- ing the elections. Very recently we saw an increase in political cybersquatting, a phenomenon we reported in [28]. And even more recently, […] we discovered the existence of Pre-fabricated Twitter factories, an effort to provide collaborators pre-compiled tweets that will attack members of the Media while avoiding detection of automatic spam algorithms from Twitter.

The theoretical foundations for a trustworthiness system:

“Our concept of trustworthiness comes from the epistemology of knowledge. When we believe that some piece of information is trustworthy (e.g., true, or mostly true), we do so for intrinsic and/or extrinsic reasons. Intrinsic reasons are those that we acknowledge because they agree with our own prior experience or belief. Extrinsic reasons are those that we accept because we trust the conveyor of the information. If we have limited information about the conveyor of information, we look for a combination of independent sources that may support the information we receive (e.g., we employ “triangulation” of the information paths). In the design of our system we aim to automatize as much as possible the process of determining the reasons that support the information we receive.”

“We define as trustworthy, information that is deemed reliable enough (i.e., with some probability) to justify action by the receiver in the future. In other words, trustworthiness is observable through actions.”

“The overall trustworthiness of the information we receive is determined by a linear combination of (a) the reputation RZ of the original sender Z, (b) the credibility we associate with the contents of the message itself C(m), and (c) characteristics of the path that the message used to reach us.”

“To compute the trustworthiness of each message from scratch is clearly a huge task. But the research that has been done so far justifies optimism in creating a semi-automatic, personalized tool that will help its users make sense of the information they receive. Clearly, no such system exists right now, but components of our system do exist in some of the popular [real-time information channels]. For a testing and evaluation of our system we plan to use primarily Twitter, but also real-time Google results and Facebook.”

In order to provide trails of trustworthiness in real-time streams, the authors plan to address the following challenges:

•  “Establishment of new metrics that will help evaluate the trustworthiness of information people receive, especially from real-time sources, which may demand immediate attention and action. […] we show that coverage of a wider range of opinions, along with independence of results’ provenance, can enhance the quality of organic search results. We plan to extend this work in the area of real-time information so that it does not rely on post-processing procedures that evaluate quality, but on real-time algorithms that maintain a trail of trustworthiness for every piece of information the user receives.”

• “Monitor the evolving ways in which information reaches users, in particular citizens near election time.”

•  “Establish a personalizable model that captures the parameters involved in the determination of trustworthiness of in- formation in real-time information channels, such as Twitter, extending the work of measuring quality in more static information channels, and by applying machine learning and data mining algorithms. To implement this task, we will design online algorithms that support the determination of quality via the maintenance of trails of trustworthiness that each piece of information carries with it, either explicitly or implicitly. Of particular importance, is that these algorithms should help maintain privacy for the user’s trusting network.”

• “Design algorithms that can detect attacks on [real-time information channels]. For example we can automatically detect bursts of activity re- lated to a subject, source, or non-independent sources. We have already made progress in this area. Recently, we advised and provided data to a group of researchers at Indiana University to help them implement “truthy”, a site that monitors bursty activity on Twitter.  We plan to advance, fine-tune and automate this process. In particular, we will develop algorithms that calculate the trust in an information trail based on a score that is affected by the influence and trustworthiness of the informants.”

In conclusion, the authors “mention that in a month from this writing, Ushahidi […] plans to release SwiftRiver, a platform that ‘enables the filtering and verification of real-time data from channels like Twitter, SMS, Email and RSS feeds’. Several of the features of Swift River seem similar to what we propose, though a major difference appears to be that our design is personalization at the individual user level.”

Indeed, having been involved in SwiftRiver research since early 2009 and currently testing the private beta, there are important similarities and some differences. But one such difference is not personalization. Indeed, Swift allows full personalization at the individual user level.

Another is that we’re hoping to go beyond just text-based information with Swift, i.e., we hope to pull in pictures and video footage (in addition to Tweets, RSS feeds, email, SMS, etc) in order to cross-validate information across media, which we expect will make the falsification of crowdsourced information more challenging, as I argue here. In any case, I very much hope that the system being developed by the authors will be free and open source so that integration might be possible.

A copy of the paper is available here (PDF). I hope to meet the authors at the Berkman Center’s “Truth in Digital Media Symposium” and highly recommend the wiki they’ve put together with additional resources. I’ve added the majority of my research on verification of crowdsourced information to that wiki, such as my 20-page study on “Information Forensics: Five Case Studies on How to Verify Crowdsourced Information from Social Media.”

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Information Forensics: Five Case Studies on How to Verify Crowdsourced Information from Social Media

Posted on November 29, 2011 | 24 comments

My 20+ page study on verifying crowdsourced information is now publicly available here as a PDF and here as an open Google Doc for comments. I very much welcome constructive feedback from iRevolution readers so I can improve the piece before it gets published in an edited book next year.

Abstract

False information can cost lives. But no information can also cost lives, especially in a crisis zone. Indeed, information is perishable so the potential value of information must be weighed against the urgency of the situation. Correct information that arrives too late is useless. Crowdsourced information can provide rapid situational awareness, especially when added to a live crisis map. But information in the social media space may not be reliable or immediately verifiable. This may explain why humanitarian (and news) organizations are often reluctant to leverage crowdsourced crisis maps. Many believe that verifying crowdsourced information is either too challenging or impossible. The purpose of this paper is to demonstrate that concrete strategies do exist for the verification of geo-referenced crowdsourced social media information. The study first provides a brief introduction to crisis mapping and argues that crowdsourcing is simply non-probability sampling. Next, five case studies comprising various efforts to verify social media are analyzed to demonstrate how different verification strategies work. The five case studies are: Andy Carvin and Twitter; Kyrgyzstan and Skype; BBC’s User-Generated Content Hub; the Standby Volunteer Task Force (SBTF); and U-Shahid in Egypt. The final section concludes the study with specific recommendations.

Update: See also this link and my other posts on Information Forensics.

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Posted in Big Data, Crisis Mapping, Crowdsourcing, Information Forensics

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How to Verify Social Media Content: Some Tips and Tricks on Information Forensics

Posted on June 21, 2011 | 35 comments

Update: I have authored a 20+ page paper on verifying social media content based on 5 case studies. Please see this blog post for a copy.

I get this question all the time: “How do you verify social media data?” This question drives many of the conversations on crowdsourcing and crisis mapping these days. It’s high time that we start compiling our tips and tricks into an online how-to-guide so that we don’t have to start from square one every time the question comes up. We need to build and accumulate our shared knowledge in information forensics. So here is the Google Doc version of this blog post, please feel free to add your best practices and ask others to contribute. Feel free to also add links to other studies on verifying social media content.

If every source we monitored in the social media space was known and trusted, then the need for verification would not be as pronounced. In other words, it is the plethora and virtual anonymity of sources that makes us skeptical of the content they deliver. The process of verifying  social media data thus requires a two-step process: the authentication of the source as reliable and the triangulation of the content as valid. If we can authenticate the source and find it trustworthy, this may be sufficient to trust the content and mark is a verified depending on context. If source authentication is difficult to ascertain, then we need to triangulate the content itself.

Lets unpack these two processes—authentication and triangulation—and apply them to Twitter since the most pressing challenges regarding social media verification have to do with eyewitness, user-generated content. The first step is to try and determine whether the source is trustworthy. Here are some tips on how to do this:

  • Bio on Twitter: Does the source provide a name, picture, bio and any  links to their own blog, identity, professional occupation, etc., on their page? If there’s a name, does searching for this name on Google provide any further clues to the person’s identity? Perhaps a Facebook page, a professional email address, a LinkedIn profile?
  • Number of Tweets: Is this a new Twitter handle with only a few tweets? If so, this makes authentication more difficult. Arasmus notes that “the more recent, the less reliable and the more likely it is to be an account intended to spread disinformation.” In general, the longer the Twitter handle has been around and the more Tweets linked to this handle, the better. This gives a digital trace, a history of prior evidence that can be scrutinized for evidence of political bias, misinformation, etc. Arasmus specifies: “What are the tweets like? Does the person qualify his/her reports? Are they intelligible? Is the person given to exaggeration and inconsistencies?”
  • Number of followers: Does the source have a large following? If there are only a few, are any of the followers know and credible sources? Also, how many lists has this Twitter hanlde been added to?
  • Number following: How many Twitter users does the Twitter handle follow? Are these known and credible sources?
  • Retweets: What type of content does the Twitter handle retweet? Does the Twitter handle in question get retweeted by known and credible sources?
  • Location: Can the source’s geographic location be ascertained? If so, are they nearby the unfolding events? One way to try and find out by proxy is to examine during which periods of the day/night the source tweets the most. This may provide an indication as to the person’s time zone.
  • Timing: Does the source appear to be tweeting in near real-time? Or are there considerable delays? Does anything appear unusual about the timing of the person’s tweets?
  • Social authentication: If you’re still unsure about the source’s reliability, use your own social network–Twitter, Facebook, LinkedIn–to find out if anyone in your network know about the source’s reliability.
  • Media authentication: Is the source quoted by trusted media outlines whether this be in the mainstream or social media space?
  • Engage the source: Tweet them back and ask them for further information. NPR’s Andy Carvin has employed this technique particularly well. For example, you can tweet back and ask for the source of the report and for any available pictures, videos, etc. Place the burden of proof on the source.

These are some of the tips that come to mind for source authentication. For more thoughts on this process, see my previous blog post “Passing the I’m-Not-Gaddafi-Test: Authenticating Identity During Crisis Mapping Operations.” If you some tips of your own not listed here, please do add them to the Google Doc—they don’t need to be limited to Twitter either.

Now, lets say that we’ve gone through list above and find the evidence inconclusive. We thus move to try and triangulate the content. Here are some tips on how to do this:

  • Triangulation: Are other sources on Twitter or elsewhere reporting on the event you are investigating? As Arasmus notes, “remain skeptical about the reports that you receive. Look for multiple reports from different unconnected sources.” The more independent witnesses you can get information from the better and the less critical the need for identity authentication.
  • Origins: If the user reporting an event is not necessarily the original source, can the original source be identified and authenticated? In particular, if the original source is found, does the time/date of the original report make sense given the situation?
  • Social authentication: Ask members of your own social network whether the tweet you are investigating is being reported by other sources. Ask them how unusual the event reporting is to get a sense of how likely it is to have happened in the first place. Andy Carvin’s followers, for example, “help him translate, triangulate, and track down key information. They enable remarkable acts of crowdsourced verification […] but he must always tell himself to check and challenge what he is told.”
  • Language: Andy Carvin notes that tweets that sound too official, using official language like “breaking news”, “urgent”, “confirmed” etc. need to be scrutinized. “When he sees these terms used, Carvin often replies and asks for additional details, for pictures and video. Or he will quote the tweet and add a simple one word question to the front of the message: Source?” The BBC’s UGC (user-generated content) Hub in London also verifies whether the vocabulary, slang, accents are correct for the location that a source might claim to be reporting from.
  • Pictures: If the twitter handle shares photographic “evidence”, does the photo provide any clues about the location where it was taken based on buildings, signs, cars, etc., in the background? The BBC’s UGC Hub checks weaponry against those know for the given country and also looks for shadows to determine the possible time of day that a picture was taken. In addition, they examine weather reports to “confirm that the conditions shown fit with the claimed date and time.” These same tips can be applied to Tweets that share video footage.
  • Follow up: If you have contacts in the geographic area of interest, then you could ask them to follow up directly/in-person to confirm the validity of the report. Obviously this is not always possible, particularly in conflict zones. Still, there is increasing anecdotal evidence that this strategy is being used by various media organizations and human rights groups. One particularly striking example comes from Kyrgyzstan where  a Skype group with hundreds of users across the country were able disprove and counter rumors at a breathtaking pace. See this blog post for more details. See my blog post on “How to Use Technology to Counter Rumors During Crises: Anecdotes from Kyrgyzstan.”

These are just a handful of tips and tricks come to mind. The number of bullet points above clearly shows we are not completely powerless when verifying social media data. There are several strategies available. The main challenge, as the BBC points out, is that this type of information forensics “can take anything from seconds […] to hours, as we hunt for clues and confirmation.” See for example my earlier post on “The Crowdsourcing Detective: Crisis, Deception and Intrigue in the Twitterspehere” which highlights some challenges but also new opportunities.

One of Storyful‘s comparative strengths when it comes to real-time news curation is the growing list of authenticated users it follows. This represents more of a bounded (but certainly not static) approach.  As noted in my previous blog post on “Seeking the Trustworthy Tweet,” following a bounded model presents some obvious advantages. This explains by the BBC recommends “maintaining lists of previously verified material [and sources] to act as a reference for colleagues covering the stories.” This strategy is also employed by the Verification Team of the Standby Volunteer Task Force (SBTF).

In sum, I still stand by my earlier blog post entitled “Wag the Dog: How Falsifying Crowdsourced Data can be a Pain.” I also continue to stand by my opinion that some data–even if not immediately verifiable—is better than no data. Also, it’s important to recognize that  we have in some occasions seen social media prove to be self-correcting, as I blogged about here. Finally, we know that information is often perishable in times of crises. By this I mean that crisis data often has a “use-by date” after which, it no longer matters whether said information is true or not. So speed is often vital. This is why semi-automated platforms like SwiftRiver that aim to filter and triangulate social media content can be helpful.

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