How to ask for Favor
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Presentation discusses the research paper titles "How to ask for a Favor: A Case Study on the Success of Altruistic Requests" by the authors "Tim Althoff, Christian, Dan Jurafsky". This is just a university class presentation to discuss the research paper and all the credits and rights are with the original authors only
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How to ask for Favor
- 1. Presented By: Ramaninder Singh Jhajj Original Authors of the Research Paper: Tim Althoff, Christian, Dan Jurafsky
- 2. • Requests : Act of asking formally for something. • Core of many social media systems. • Factors that lead members to satisfy a request remain largely unknown. How to Ask for a Favor : A Case Study on the Success of Altruistic Requests 2
- 3. • Does the Language of the Request Matters? • If Yes, How it matters? • Is it possible to predict the success or failure of Altruistic Requests. No Incentive in return for the favor How to Ask for a Favor : A Case Study on the Success of Altruistic Requests 3
- 4. • Goal was to understand what motivates people to give when they do not receive anything tangible in return. • Developed a framework for controlling potential confounds while studying the role of two aspects that characterize compelling requests: – Social Factors – Linguistic Factors How to Ask for a Favor : A Case Study on the Success of Altruistic Requests 4
- 5. How to Ask for a Favor : A Case Study on the Success of Altruistic Requests 5
- 6. • What is being requested? • What the giver receives in return? • Group Dynamics • In peer-to-peer, people are more likely to give to projects what others are already giving to. (Mitra and Gilbert 2014; Mollick 2014; Etter, Grossglauser, and Thiran 2013; Ceyhan, Shi, and Leskovec 2011; Teevan, Morris, and Panovich 2011; Burke et al. 2007; Wash 2013; Cialdini 2001) 6
- 7. • Online community facilitating sending and receiving free pizzas between strangers. • As they say „Together we aim to restore faith in humanity, one slice at a time“ How to Ask for a Favor : A Case Study on the Success of Altruistic Requests 7
- 8. • What? – All requests ask for same thing, a pizza. • Incentive? – No incentives or rewards • Group Dynamics? – Social Networking, Requests are largely Textual • Request Satisfiers? – Single User How to Ask for a Favor : A Case Study on the Success of Altruistic Requests 8
- 9. • 21,577 posts (8th Dec. 2010 – 29th Sept. 2012) • Only users with a single request, 5728 requests. • Average success rate 24.6% • Split Dataset mirrioring the success rate: – Development (70%) – Test Set (30%) How to Ask for a Favor : A Case Study on the Success of Altruistic Requests 9
- 10. How to Ask for a Favor : A Case Study on the Success of Altruistic Requests 10
- 11. 1. Textual Factors – Politeness – Evidentiality – Reciprocity – Sentiment – Length 2. Social Factors – Status – Similarity 3. Narratives – Money – Job – Student – Family – Craving How to Ask for a Favor : A Case Study on the Success of Altruistic Requests 11
- 12. • “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 [...] would certainly return the favor again when I am able to reciprocate.” Length : Favorable for success Evidentiality : Urgents requests are met fast. Reciprocity : Promise to return the favor How to Ask for a Favor : A Case Study on the Success of Altruistic Requests 12
- 13. • Temporal Factors : Temporal or Seasonal effects are controlled • Politeness : Measure politeness by extracting 19 individual features deom computational politenes model (Danescu- Niculescu-Mizil et al. 2013). • Evidentiality : Presence of an image link, providing evidence for their claim (86% of images in random sample included some kind of evidence) • Reciprocity : If the request includes phrases like „pay it forward“, „pay it back“ or „return the favor“. How to Ask for a Favor : A Case Study on the Success of Altruistic Requests 13
- 14. • Sentiment : Extracted sentiment annotations for each sentence of the request using the Stanford CoreNLP Package, count features based on lexicons of positive and negative words from LIWC, detecting emoticons. • Length: Total number of words in request. • Status : Karma points, measure whether or not uses has posted on RAOP before, user account age. • Narrative : measure usage of all 5 narratives by word count features that measures how often a given requests mention words from the previously defined nattative lexicons. How to Ask for a Favor : A Case Study on the Success of Altruistic Requests 14
- 15. Coefficient Estimate SE Community Age -0.13*** 0.01 First Half of Month 0.22** 0.08 Gratitude 0.27** 0.08 Including Image 0.81*** 0.17 Reciprocity 0.32** 0.10 Strong Positive Sentiment 0.14 0.08 Strong Negative Sentiment -0.07 0.08 Length in 100 words 0.30*** 0.05 ***p < 0.001, **p < 0.01, *p < 0.05 Temporal Control Politeness Evidentiality Reciprocity Length Sentiment How to Ask for a Favor : A Case Study on the Success of Altruistic Requests 15
- 16. Coefficient Estimate SE Karma 0.13*** 0.02 Posted in RAOP before 1.34*** 0.16 Narrative Craving -0.34*** 0.09 Narrative Family 0.22* 0.09 Narrative Job 0.26** 0.09 Narrative Money 0.19** 0.08 Narrative Student 0.09 0.09 ***p < 0.001, **p < 0.01, *p < 0.05 Status Narratives How to Ask for a Favor : A Case Study on the Success of Altruistic Requests 16
- 17. Length : 50 words Narrative : Craving Length: 50 words Narrative : Job and Money Length : 150 words Narrative : Job and Money Includes Picture, Gratitute and Reciprocity How to Ask for a Favor : A Case Study on the Success of Altruistic Requests 17
- 18. How to Ask for a Favor : A Case Study on the Success of Altruistic Requests 18
- 19. • Predicting held-out requests (1.6k) • Model: Area under receiver operating characteristic curve (ROC AUC) – Illustrate performance of binary classifier systems. – Graphical plot. – True Positive Rate VS False Positive Rate (Friedman, Hastie, and Tibshirani 2010; DeLong, DeLong, and Clarke-Pearson 1988) 19
- 20. How to Ask for a Favor : A Case Study on the Success of Altruistic Requests Feature ROC AUC (***p < 0.001) Random Baseline 0.500 Unigram Baseline 0.621*** Bigram Baseline 0.618*** Trigram Baseline 0.618*** Text Features 0.625*** Social Features 0.576*** Temporal Features 0.579*** Temporal + Social 0.638*** Temporal + Social + Text 0.669*** Temporal + Social + Text + Unigram 0.672*** 20
- 21. • User similarity (in terms of Interest and Activity)had NO significant effect on giving. • Did not included user similarity as a feature in the logistic regression model since authors were able to observe givers for a small subset. 21
- 22. • Language of requests matter a lot. • Narratives of request also play a role in success. • Reciprocity matters : Promise to pay it forward • Pro-social behavior towards requestors who are of high status. How to Ask for a Favor : A Case Study on the Success of Altruistic Requests 22
- 23. • Expressing gratitude is also vital. • And finaly we can say: Success is Predictable! How to Ask for a Favor : A Case Study on the Success of Altruistic Requests 23
- 24. How to Ask for a Favor : A Case Study on the Success of Altruistic Requests 24
- 25. 25
Editor's Notes
- We live in a time where people increasingly turn to the web for help. And if we do not get satisfactory answers from the existing web pages, what we do. We turn to real people but still on the web. We ask question in online forums. These requests make the core of many social media systems such as stackoverflow.com, donorschoose.org, reddit.com etc. Factors that lead the community members to satisfy a request remain largely unknown. If we can understand these dynamics and factors, users can be educated for better formulating the requests, it has usages in social psychology and linguistic pragmatics.
- This paper analysis the language of the requests. So main questions which the authors tried to answer in this paper are:
- How to distabgle(control) the effects of these factors and focus on language? Is there a possibilits to find such community or such setup where these complexities are addressed?
- Lets see what are the characteristics of this community. How this community resolves the confounds/complexities we are facing.
- This community help in controlling all the confounds and provides us with an unusually clear picture of the effects of the language and social factors on success. This is the Ideal situation to understand the effect of Language
- Language of the request matters a lot specially in these kind of communities where request are Altruistic in nature.
- Authors divided the factors in 3 main categories: Politeness : A person experiencing gratitude is more likely to behave prosocially. However gratitude is only one component of politeness, others are deference, greetings, apologies etc. Authors try to answer a more general question here: does a polite request make you more likely to be successful? Evidentiality : Urgent requests are met more frequently than non-urgent requests. Reciprocity : Generalized reciprocity (forward to another community member) Sentiment : Behavioral literature points out towards the sentiments of the persons involved. But here we can only refer to the sentiments of the text. Length: Longer request will be interpreted showing more effort, and gives opportunity to provide more convincing evidence of their situation. Status: Studies in solial psychology found that high status attracts help more often. Similarity : People are more likely to help those who reseble them. Narratives: Different kinds of narratives are identified based on previous literature using topic modeling and related techniques. We came to know that success rate varies a lot with different cluster of words (narrative groups). We found that some topics cover the same or multiple narratives (some noise) and that some mostly consist of function/stop words etc. Therefore, we used those topics at a starting point (together with similar LIWC categories) to manually define the five narratives. Dropped are Friend, Time, Gratitude, Pizza, General.
- Temporal Factors: Controlled by measuring specific week days, months, hours etc.
- Linguistic Inquiry and Word Count (LIWC) is a text analysis software program
- All these factors are measure and success probability of a request is modeled in a logistic regression framework that allows to reason about the significance of one factor given all the other factors using Success as a dependent variable and textual, social and temporal features as independent variables. The likelihood-ratio test discussed above to assess model fit is also the recommended procedure to assess the contribution of individual "predictors" to a given model. If the p-value is small enough to claim statistical significance, that just means there is strong evidence that the coefficient is different from 0. Politeness: Out of 19 fetures, only gratitude is significant. Evidentiality: High significance, make success more likely to succeed. Need and Urgency Reciprocity: Willingness to give back to the community, high significance. Sentiment: Stops being significantly correlated with success when controlling for other variables.
- Status: Account age is strongly correlated with karma, senior users have high status. Narratives: Narratives significantly improve the fix except the „Student“ narrative. Narratives that clearly communicate need are more successful than those that do not.
- Lets take an example to understand the results and interpretate. Median Length is 74 words. Examples assume Median Karma and Community age.
- We have seen that textual, social and temporal features all significantly improve the fit of logistic regression model. Lets test to what degree the model is able to generalize and predict the success of unseen requests from held out test data set. L1 penalized estimation method shrinks the estimates of the regression coefficients towards 0 relative to the maximum likelihood estimates. Receiver operating characteristic is a graphical plot which illustrates the performance of a binary classifier system. It is created by plotting the true positive rate vs false positive rate. A perfect model will score 1 and random guess will score around 0.5
- In the fields of computational linguistics and probability, an n-gram is a contiguous sequence of n items from a given sequence of text or speech. DNA sequencingbase pair …AGCTTCGA……, (Unigram) A, G, C, T, T, C, G, A, ……, (Bigram)AG, GC, CT, TT, TC, CG, GA, ……, (Trigram)AGC, GCT, CTT, TTC, TCG, CGA, … No significance difference between textual model and uni, bi and trigram baselines even when we have only 9 features in tectual and baselines have much more features. Lastly, table also demostrate that unigram model does not significantly improve predective accuracy. This shows that the concise set of textual factors accounts for almost all the variances. It is worth pointing out that we are purposfully dealing with a very difficult setting- since the goal is to assist the users during request creation we do not use any factors that can only be observed later (e.g. Responses, updates, comments).
- Measured user similarity by representing users by their interests in terms of the set of subreddits in which they have posted at least once.