Forget GMOs. The Future of Food Is Data—Mountains of It

Curated on Posted on by Stefaan Verhulst

Cade Metz at Wired: “… Led by Dan Zigmond—who previously served as chief data scientist for YouTube, then Google Maps—this ambitious project aims to accelerate the work of all the biochemists, food scientists, and chefs on the first floor, providing a computer-generated shortcut to what Hampton Creek sees as the future of food. “We’re looking at the whole process,” Zigmond says of his data team, “trying to figure out what it all means and make better predictions about what is going to happen next.”

The project highlights a movement, spreading through many industries, that seeks to supercharge research and development using the kind of data analysis and manipulation pioneered in the world of computer science, particularly at places like Google and Facebook. Several projects already are using such techniques to feed the development of new industrial materials and medicines. Others hope the latest data analytics and machine learning techniques can help diagnosis disease. “This kind of approach is going to allow a whole new type of scientific experimentation,” says Jeremy Howard, who as the president of Kaggle once oversaw the leading online community of data scientists and is now applying tricks of the data trade to healthcare as the founder of Enlitic.
Zigmond’s project is the first major effort to apply “big data” to the development of food, and though it’s only just getting started—with some experts questioning how effective it will be—it could spur additional research in the field. The company may license its database to others, and Hampton Creek founder and CEO Josh Tetrick says it may even open source the data, so to speak, freely sharing it with everyone. “We’ll see,” says Tetrick, a former college football linebacker who founded Hampton Creek after working on economic and social campaigns in Liberia and Kenya. “That would be in line with who we are as a company.”…
Initially, Zigmond and his team will model protein interactions on individual machines, using tools like the R programming language (a common means of crunching data) and machine learning algorithms much like those that recommend products on Amazon.com. As the database expands, they plan to arrange for much larger and more complex models that run across enormous clusters of computer servers, using the sort of sweeping data-analysis software systems employed by the likes of Google. “Even as we start to get into the tens and hundreds of thousands and millions of proteins,” Zigmond says, “it starts to be more than you can handle with traditional database techniques.”
In particular, Zigmond is exploring the use of deep learning, a form of artificial intelligence that goes beyond ordinary machine learning. Google is using deep learning to drive the speech recognition system in Android phones. Microsoft is using it to translate Skype calls from one language to another. Zigmond believes it can help model the creation of new foods….”

A Few Useful Things to Know about Machine Learning

Curated on Posted on by Stefaan Verhulst

A new research paper by Pedro Domingos: “Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. This is often feasible and cost-effective where manual programming is not. As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer science and other fields. However, developing successful machine learning applications requires a substantial amount of “black art” that is hard to find in textbooks. This article summarizes twelve key lessons that machine learning researchers and practitioners have learned. These include pitfalls to avoid, important issues to focus on, and answers to common questions.”
 

Google's fact-checking bots build vast knowledge bank

Curated on Posted on by Stefaan Verhulst

Hal Hodson in the New Scientist: “The search giant is automatically building Knowledge Vault, a massive database that could give us unprecedented access to the world’s facts

GOOGLE is building the largest store of knowledge in human history – and it’s doing so without any human help. Instead, Knowledge Vault autonomously gathers and merges information from across the web into a single base of facts about the world, and the people and objects in it.

The breadth and accuracy of this gathered knowledge is already becoming the foundation of systems that allow robots and smartphones to understand what people ask them. It promises to let Google answer questions like an oracle rather than a search engine, and even to turn a new lens on human history.

Knowledge Vault is a type of “knowledge base” – a system that stores information so that machines as well as people can read it. Where a database deals with numbers, a knowledge base deals with facts. When you type “Where was Madonna born” into Google, for example, the place given is pulled from Google’s existing knowledge base.

This existing base, called Knowledge Graph, relies on crowdsourcing to expand its information. But the firm noticed that growth was stalling; humans could only take it so far. So Google decided it needed to automate the process. It started building the Vault by using an algorithm to automatically pull in information from all over the web, using machine learning to turn the raw data into usable pieces of knowledge.

Knowledge Vault has pulled in 1.6 billion facts to date. Of these, 271 million are rated as “confident facts”, to which Google’s model ascribes a more than 90 per cent chance of being true. It does this by cross-referencing new facts with what it already knows.

“It’s a hugely impressive thing that they are pulling off,” says Fabian Suchanek, a data scientist at Télécom ParisTech in France.

Google’s Knowledge Graph is currently bigger than the Knowledge Vault, but it only includes manually integrated sources such as the CIA Factbook.

Knowledge Vault offers Google fast, automatic expansion of its knowledge – and it’s only going to get bigger. As well as the ability to analyse text on a webpage for facts to feed its knowledge base, Google can also peer under the surface of the web, hunting for hidden sources of data such as the figures that feed Amazon product pages, for example.

Tom Austin, a technology analyst at Gartner in Boston, says that the world’s biggest technology companies are racing to build similar vaults. “Google, Microsoft, Facebook, Amazon and IBM are all building them, and they’re tackling these enormous problems that we would never even have thought of trying 10 years ago,” he says.

The potential of a machine system that has the whole of human knowledge at its fingertips is huge. One of the first applications will be virtual personal assistants that go way beyond what Siri and Google Now are capable of, says Austin…”

Towards Timely Public Health Decisions to Tackle Seasonal Diseases With Open Government Data

Curated on Posted on by Stefaan Verhulst
Paper by Vandana Srivastava and Biplav Srivastava for the Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence : “Improving public health is a major responsibility of any government, and is of major interest to citizens and scientific communities around the world. Here, one sees two extremes. On one hand, tremendous progress has been made in recent years in the understanding of causes, spread and remedies of common and regularly occurring diseases like Dengue, Malaria and Japanese Encephalistis (JE). On the other hand, public agencies treat these diseases in an ad hoc manner without learning from the experiences of previous years. Specifically, they would get alerted once reported cases have already arisen substantially in the known disease season, reactively initiate a few actions and then document the disease impact (cases, deaths) for that period, only to forget this learning in the next season. However, they miss the opportunity to reduce preventable deaths and sickness, and their corresponding economic impact, which scientific progress could have enabled. The gap is universal but very prominent in developing countries like India.
In this paper, we show that if public agencies provide historical disease impact information openly, it can be analyzed with statistical and machine learning techniques, correlated with best emerging practices in disease control, and simulated in a setting to optimize social benefits to provide timely guidance for new disease seasons and regions. We illustrate using open data for mosquito-borne communicable diseases; published results in public health on efficacy of Dengue control methods and apply it on a simulated typical city for maximal benefits with available resources. The exercise helps us further suggest strategies for new regions that may be anywhere in the world, how data could be better recorded by city agencies and what prevention methods should medical community focus on for wider impact.
Full Text: PDF“

Selected Readings on Sentiment Analysis

Curated on Posted on by sophie

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of sentiment analysis was originally published in 2014.

Sentiment Analysis is a field of Computer Science that uses techniques from natural language processing, computational linguistics, and machine learning to predict subjective meaning from text. The term opinion mining is often used interchangeably with Sentiment Analysis, although it is technically a subfield focusing on the extraction of opinions (the umbrella under which sentiment, evaluation, appraisal, attitude, and emotion all lie).

The rise of Web 2.0 and increased information flow has led to an increase in interest towards Sentiment Analysis — especially as applied to social networks and media. Events causing large spikes in media — such as the 2012 Presidential Election Debates — are especially ripe for analysis. Such analyses raise a variety of implications for the future of crowd participation, elections, and governance.

Selected Reading List (in alphabetical order)

Annotated Selected Reading List (in alphabetical order)

Choi, Eunsol et al. “Hedge detection as a lens on framing in the GMO debates: a position paper.” Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics 13 Jul. 2012: 70-79. http://bit.ly/1wweftP

  • Understanding the ways in which participants in public discussions frame their arguments is important for understanding how public opinion is formed. This paper adopts the position that it is time for more computationally-oriented research on problems involving framing. In the interests of furthering that goal, the authors propose the following question: In the controversy regarding the use of genetically-modified organisms (GMOs) in agriculture, do pro- and anti-GMO articles differ in whether they choose to adopt a more “scientific” tone?
  • Prior work on the rhetoric and sociology of science suggests that hedging may distinguish popular-science text from text written by professional scientists for their colleagues. The paper proposes a detailed approach to studying whether hedge detection can be used to understand scientific framing in the GMO debates, and provides corpora to facilitate this study. Some of the preliminary analyses suggest that hedges occur less frequently in scientific discourse than in popular text, a finding that contradicts prior assertions in the literature.

Michael, Christina, Francesca Toni, and Krysia Broda. “Sentiment analysis for debates.” (Unpublished MSc thesis). Department of Computing, Imperial College London (2013). http://bit.ly/Wi86Xv

  • This project aims to expand on existing solutions used for automatic sentiment analysis on text in order to capture support/opposition and agreement/disagreement in debates. In addition, it looks at visualizing the classification results for enhancing the ease of understanding the debates and for showing underlying trends. Finally, it evaluates proposed techniques on an existing debate system for social networking.

Murakami, Akiko, and Rudy Raymond. “Support or oppose?: classifying positions in online debates from reply activities and opinion expressions.” Proceedings of the 23rd International Conference on Computational Linguistics: Posters 23 Aug. 2010: 869-875. https://bit.ly/2Eicfnm

  • In this paper, the authors propose a method for the task of identifying the general positions of users in online debates, i.e., support or oppose the main topic of an online debate, by exploiting local information in their remarks within the debate. An online debate is a forum where each user posts an opinion on a particular topic while other users state their positions by posting their remarks within the debate. The supporting or opposing remarks are made by directly replying to the opinion, or indirectly to other remarks (to express local agreement or disagreement), which makes the task of identifying users’ general positions difficult.
  • A prior study has shown that a link-based method, which completely ignores the content of the remarks, can achieve higher accuracy for the identification task than methods based solely on the contents of the remarks. In this paper, it is shown that utilizing the textual content of the remarks into the link-based method can yield higher accuracy in the identification task.

Pang, Bo, and Lillian Lee. “Opinion mining and sentiment analysis.” Foundations and trends in information retrieval 2.1-2 (2008): 1-135. http://bit.ly/UaCBwD

  • This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Its focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. It includes material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.

Ranade, Sarvesh et al. “Online debate summarization using topic directed sentiment analysis.” Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining 11 Aug. 2013: 7. http://bit.ly/1nbKtLn

  • Social networking sites provide users a virtual community interaction platform to share their thoughts, life experiences and opinions. Online debate forum is one such platform where people can take a stance and argue in support or opposition of debate topics. An important feature of such forums is that they are dynamic and grow rapidly. In such situations, effective opinion summarization approaches are needed so that readers need not go through the entire debate.
  • This paper aims to summarize online debates by extracting highly topic relevant and sentiment rich sentences. The proposed approach takes into account topic relevant, document relevant and sentiment based features to capture topic opinionated sentences. ROUGE (Recall-Oriented Understudy for Gisting Evaluation, which employ a set of metrics and a software package to compare automatically produced summary or translation against human-produced onces) scores are used to evaluate the system. This system significantly outperforms several baseline systems and show improvement over the state-of-the-art opinion summarization system. The results verify that topic directed sentiment features are most important to generate effective debate summaries.

Schneider, Jodi. “Automated argumentation mining to the rescue? Envisioning argumentation and decision-making support for debates in open online collaboration communities.” http://bit.ly/1mi7ztx

  • Argumentation mining, a relatively new area of discourse analysis, involves automatically identifying and structuring arguments. Following a basic introduction to argumentation, the authors describe a new possible domain for argumentation mining: debates in open online collaboration communities.
  • Based on our experience with manual annotation of arguments in debates, the authors propose argumentation mining as the basis for three kinds of support tools, for authoring more persuasive arguments, finding weaknesses in others’ arguments, and summarizing a debate’s overall conclusions.

Microsoft Unveils Machine Learning for the Masses

Curated on Posted on by Stefaan Verhulst

Quentin Hardy in the New York Times: “Microsoft has a new strategy to win cloud business: A supposedly comprehensive predictive analysis service — and all you have to do is store your data in Azure, the Microsoft cloud.

The service, called Microsoft Azure Machine Learning, was announced Monday but won’t be available until July. It combines Microsoft’s own software with publicly available open source software, packaged in a way that is easier to use than most of the arcane strategies currently in use.
“This is drag-and-drop software,” said Joseph Sirosh, vice president for machine learning at Microsoft. “My high schooler is using this.”
That would be a big step forward in popularizing what is currently a difficult process in increasingly high demand. It would also further the ambitions of Satya Nadella, Microsoft’s chief executive, of making Azure the center of Microsoft’s future.
Users of Azure Machine Learning will have to keep their data in Azure, and Microsoft will provide ways to move data from competing services, like Amazon Web Services. Pricing has not yet been finalized, Mr. Sirosh said, but will be based on a premium to Azure’s standard computing and transmission charges.
Machine learning computers examine historical data through different algorithms and programming languages to make predictions. The process is commonly used in Internet search, fraud detection, product recommendations and digital personal assistants, among other things.
As more data is automatically stored online, there are opportunities to use machine learning for performing maintenance, scheduling hospital services, and anticipating disease outbreaks and crime, among other things. The methods have to become easier and cheaper to be popular, however.
That is the goal of Azure Machine Learning. “This is, as far as I know, the first comprehensive machine learning service in the cloud,” Mr. Sirosh said. “I’m leveraging every asset in Microsoft for this.” He is also using ways of accessing an open source version of R, a standard statistical language, while in Azure.
Microsoft is likely to face competition from rival cloud companies, including Google and Amazon. Both Google and Amazon have things like data frameworks used in building machine learning algorithms, as well as their own analysis services. IBM is eager to make use of its predictive software in its cloud business. Visualization companies like Tableau specialize in presenting the results so they can be acted on easily…”

Crowdsourcing moving beyond the fringe

Curated on Posted on by Stefaan Verhulst

Bob Brown in Networked World: ” Depending up on how you look at it, crowdsourcing is all the rage these days — think Wikipedia, X Prize and Kickstarter — or at the other extreme, greatly underused.
To the team behind the new “insight network” Yegii, crowdsourcing has not nearly reached its potential despite having its roots as far back as the early 1700s and a famous case of the British Government seeking a solution to “The Longitude Problem” in order to make sailing less life threatening. (I get the impression that mention of this example is obligatory at any crowdsourcing event.)
This angel-funded startup, headed by an MIT Sloan School of Management senior lecturer and operating from a Boston suburb, is looking to exploit crowdsourcing’s potential through a service that connects financial, healthcare, technology and other organizations seeking knowledge with experts who can provide it – and fairly fast. To CEO Trond Undheim, crowdsourcing is “no longer for fringe freelance work,” and the goal is to get more organizations and smart individuals involved.
“Yegii is essentially a network of networks, connecting people, organizations, and knowledge in new ways,” says Undheim, who explains that the name Yegii is Korean for “talk” or “discussion”. “Our focus is laser sharp: we only rank and rate knowledge that says something essential about what I see as the four forces of industry disruption: technology, policy, user dynamics and business models.  We tackle challenging business issues across domains, from life sciences to energy to finance.  The point is that today’s industry classification is falling apart. We need more specific insight than in-house strategizing or generalist consulting advice.”
Undheim attempted to drum up interest in the new business last week at an event at Babson College during which a handful of crowdsourcing experts spoke. Harvard Business School adjunct professor Alan MacCormack discussed the X Prize, Netflix Prize and other examples of spurring competition through crowdsourcing. MIT’s Peter Gloor extolled the virtue of collaborative and smart swarms of people vs. stupid crowds (such as football hooligans). A couple of advertising/marketing execs shared stories of how clients and other brands are increasingly tapping into their customer base and the general public for new ideas from slogans to products, figuring that potential new customers are more likely to trust their peers than corporate ads. Another speaker dove into more details about how to run a crowdsourcing challenge, which includes identifying motivation that goes beyond money.
All of this was to frame Yegii’s crowdsourcing plan, which is at the beta stage with about a dozen clients (including Akamai and Santander bank) and is slated for mass production later this year. Yegii’s team consists of five part-timers, plus a few interns, who are building a web-based platform that consists of “knowledge assets,” that is market research, news reports and datasets from free and paid sources. That content – on topics that range from Bitcoin’s impact on banks to telecom bandwidth costs — is reviewed and ranked through a combination of machine learning and human peers. Information seekers would pay Yegii up to hundreds of dollars per month or up to tens of thousands of dollars per project, and then multidisciplinary teams would accept the challenge of answering their questions via customized reports within staged deadlines.
“We are focused on building partnerships with other expert networks and associations that have access to smart people with spare capacity, wherever they are,” Undheim says.
One reason organizations can benefit from crowdsourcing, Undheim says, is because of the “ephemeral nature of expertise in today’s society.” In other words, people within your organization might think of themselves as experts in this or that, but when they really think about it, they might realize their level of expertise has faded. Yegii will strive to narrow down the best sources of information for those looking to come up to speed on a subject over a weekend, whereas hunting for that information across a vast search engine would not be nearly as efficient….”

A New Way to Look at Law, With Data Viz and Machine Learning

Curated on Posted on by Stefaan Verhulst

  in Wired:

Ravel displays search results as an interactive visualization. Image: Ravel
“On TV, being a lawyer is all about dazzling jurors with verbal pyrotechnics. But for many lawyers–especially young ones–the job is about research. Long, dry, tedious research.
It’s that less glamorous side of the profession that Daniel Lewis and Nik Reed are trying to upend with Ravel. Using data visualization, language analysis, and machine learning, the Stanford Law grads are aiming to reinvent legal research–and perhaps give young lawyers a deeper understanding of their field in the process.
Lawyers have long relied on subscription services like LexisNexis and WestLaw to do their jobs. These services offer indispensable access to vast databases of case documents. Lewis remembers seeing the software on the computers at his Dad’s law firm when he used to hang out there as a kid. You’d put in a keyword, say, securities fraud, and get back a long, rank-ordered list of results relevant to that topic.
Years later, when Lewis was embarking on his own legal career as a first year at Stanford Law, he was struck by how little had changed. “The tools and technologies were the same,” he says. “It was surprising and disconcerting.” Reed, his classmate there, was also perplexed, especially having spent some time in the finance industry working with its high-powered tools. “There was all this cool stuff that everyone else was using in every other field, and it just wasn’t coming to lawyers,” he says.

Early users have reported that Ravel cut their overall research time by up to two thirds….

Ravel’s most ambitious features, however, are intended to help with the analysis of cases. These tools, saved for premium subscribers, are designed to automatically surface the key passages in whatever case you happen to be looking at, sussing out instances when they’ve been cited or reinterpreted in cases that followed.
To do this, Ravel effectively has to map the law, an undertaking that involves both human insight and technical firepower. The process, roughly: Lewis and Reed will look at a particular case, pinpoint the case it’s referencing, and then figure out what ties them together. It could be a direct reference, or a glancing one. It might show up as three paragraphs in that later ruling, or just a sentence.
Once those connections have been made, they’re handed off to Ravel’s engineers. The engineers, which make up more than half of the company’s ten-person team, are tasked with building models that can identify those same sorts of linkages in other cases, using natural language processing. In effect, Ravel’s trying to uncover the subtle linguistic patterns undergirding decades of legal rulings.
That all goes well beyond visual search, and the idea of future generations of lawyers learning from an algorithmic analysis of the law seems quietly dangerous in its own way (though a sterling conceit for a near-future short story!)
Still, compared to the comparatively primitive tools that still dominate the field today, Lewis and Reed see Ravel as a promising resource for young lawyers and law students. “It’s about helping them research more confidently,” Lewis says. “It’s about making sure they understand the story in the right way.” And, of course, about making all that research a little less tedious, too.”

How Long Is Too Long? The 4th Amendment and the Mosaic Theory

Curated on Posted on by Stefaan Verhulst

Law and Liberty Blog: “Volume 8.2 of the NYU Journal of Law and Liberty has been sent to the printer and physical copies will be available soon, but the articles in the issue are already available online here. One article that has gotten a lot of attention so far is by Steven Bellovin, Renee Hutchins, Tony Jebara, and Sebastian Zimmeck titled “When Enough is Enough: Location Tracking, Mosaic Theory, and Machine Learning.” A direct link to the article is here.
The mosaic theory is a modern corollary accepted by some academics – and the D.C. Circuit Court of Appeals in Maynard v. U.S. – as a twenty-first century extension of the Fourth Amendment’s prohibition on unreasonable searches of seizures. Proponents of the mosaic theory argue that at some point enough individual data collections, compiled and analyzed together, become a Fourth Amendment search. Thirty years ago the Supreme Court upheld the use of a tracking device for three days without a warrant, however the proliferation of GPS tracking in cars and smartphones has made it significantly easier for the police to access a treasure trove of information about our location at any given time.
It is easy to see why this theory has attracted some support. Humans are creatures of habit – if our public locations are tracked for a few days, weeks, or a month, it is pretty easy for machines to learn our ways and assemble a fairly detailed report for the government about our lives. Machines could basically predict when you will leave your house for work, what route you will take, when and where you go grocery shopping, all before you even do it, once it knows your habits. A policeman could observe you moving about in public without a warrant of course, but limited manpower will always reduce the probability of continuous mass surveillance. With current technology, a handful of trained experts could easily monitor hundreds of people at a time from behind a computer screen, and gather even more information than most searches requiring a warrant. The Supreme Court indicated a willingness to consider the mosaic theory in U.S. v. Jones, but has yet to embrace it…”

The article in Law & Liberty details the need to determine at which point machine learning creates an intrusion into our reasonable expectations of privacy, and even discusses an experiment that could be run to determine how long data collection can proceed before it is an intrusion. If there is a line at which individual data collection becomes a search, we need to discover where that line is. One of the articles’ authors, Steven Bollovin, has argued that the line is probably at one week – at that point your weekday and weekend habits would be known. The nation’s leading legal expert on criminal law, Professor Orin Kerr, fired back on the Volokh Conspiracy that Bollovin’s one week argument is not in line with previous iterations of the mosaic theory.

Data Mining Reddit Posts Reveals How to Ask For a Favor–And Get it

Curated on Posted on by Stefaan Verhulst

Emerging Technology From the arXiv: “There’s a secret to asking strangers for something and getting it. Now data scientists say they’ve discovered it by studying successful requests on the web

One of the more extraordinary phenomena on the internet is the rise of altruism and of websites designed to enable it. The Random Acts of Pizza section of the Reddit website is a good example.

People leave messages asking for pizza which others fulfil if they find the story compelling. As the site says: “because… who doesn’t like helping out a stranger? The purpose is to have fun, eat pizza and help each other out. Together, we aim to restore faith in humanity, one slice at a time.”

A request might go something like this: “It’s been a long time since my mother and I have had proper food. I’ve been struggling to find any kind of work so I can supplement my mom’s social security… A real pizza would certainly lift our spirits”. Anybody can then fulfil the order which is then marked on the site with a badge saying “got pizza’d”, often with notes of thanks.

That raises an interesting question. What kinds of requests are most successful in getting a response? Today, we get an answer thanks to the work of Tim Althoff at Stanford University and a couple of pals who lift the veil on the previously murky question of how to ask for a favour—and receive it.

…

They analysed how various features might be responsible for the success of a post, such as the politeness of the post; its sentiment, whether positive or negative for example; its length. The team also looked at the similarity of the requester to the benefactor; and also the status of the requester.

Finally, they examined whether the post contained evidence of need in the form of a narrative that described why the requester needed free pizza.

Althoff and co used a standard machine learning algorithm to comb through all the possible correlations in 70 per cent of the data, which they used for training. Having found various correlations, they tested to see whether this had predictive power in the remaining 30 per cent of the data. In other words, can their algorithm predict whether a previously unseen request will be successful or not?

It turns out that their algorithm makes a successful prediction about 70 per cent of the time. That’s far from perfect but much better than random guessing which is right only half the time.

So what kinds of factors are important? Narrative is a key part of many of the posts, so Althoff and co spent some time categorising the types of stories people use.

They divided the narratives into five types, those that mention: money; a job; being a student; family; and a final group that includes mentions of friends, being drunk, celebrating and so on, which Althoff and co call ‘craving’.

Of these, narratives about jobs, family and money increase the probability of success. Student narratives have no effect while craving narratives significantly reduce the chances of success. In other words, narratives that communicate a need are more successful than those that do not.

 “We find that clearly communicating need through the narrative is essential,” say Althoff and co. And evidence of reciprocation helps too.

(Given these narrative requirements, it is not surprising that longer requests tend to be more successful than short ones.)

So for example, the following request was successful because it clearly demonstrates both need and evidence of reciprocation.

“My gf and I have hit some hard times with her losing her job and then unemployment as well for being physically unable to perform her job due to various hand injuries as a server in a restaurant. She is currently petitioning to have unemployment reinstated due to medical reasons for being unable to perform her job, but until then things are really tight and ANYTHING would help us out right now.

I’ve been both a giver and receiver in RAOP before and would certainly return the favor again when I am able to reciprocate. It took everything we have to pay rent today and some food would go a long ways towards making our next couple of days go by much better with some food.”

By contrast, the ‘craving’ narrative below demonstrates neither and was not successful.

“My friend is coming in town for the weekend and my friends and i are so excited because we haven’t seen him since junior high. we are going to a high school football game then to the dollar theater after and it would be so nice if someone fed us before we embarked :)”

Althoff and co also say that the status of the requester is an important factor too. “We find that Reddit users with higher status overall (higher karma) or higher status within the subcommunity (previous posts) are significantly more likely to receive help,” they say.

But surprisingly, being polite does not help (except by offering thanks).

That’s interesting work. Until now, psychologists have never understood the factors that make requests successful, largely because it has always been difficult to separate the influence of the request from what is being requested.

The key here is that everybody making requests in this study wants the same thing—pizza. In one swoop, this makes the data significantly easier to tease apart.

An important line of future work will be in using his work to understand altruistic behaviour in other communities too…

Ref:  http://arxiv.org/abs/1405.3282 : How to Ask for a Favor: A Case Study on the Success of Altruistic Requests”