Predicting Reputation in the Sharing Economy with Twitter Social Data
- RQ1. Can social digital footprints be used for predicting bad user behavior on sharing economy sites?
- RQ2. What are the most relevant social features for predicting reputation in the Sharing Economy?
2.1. Trust and Reputation Notions and Computational Models
2.2. Trust and Reputation in the Sharing Economy
2.3. Characterizing and Interlinking User Profiles in Social Networks
3. Case Study and Methodology
3.1. Identity Pairing
- Contain a link to a Wallapop product that we can use to reach the Wallapop profile of the seller.
- Tweets similar to the default tweet message, including keywords as selling or hashtags as #wallapop.
3.2. Data Collection and Feature Generation
3.2.1. Data Collection
- users/show: profile characteristics as name, picture, or description.
- statuses/user_timeline: list of tweets posted by the user.
- followers/list: list of followers.
- friends/list: list of friends.
- Search: a list of items available near a given location.
- Item: description of an item (including price and seller profile).
- User: user public profile, including data such as the number of reviews and verifications.
- User reviews: full set of reviews given or received by a user.
3.2.2. Feature Generation
- Profile: we selected the account creation date (in seconds since epoch) based on the hypothesis that users with active accounts for a long time are likely to be more trustable .
- Behavior: based on previous research [83,84], we have selected activity metrics: most frequent tweeting hours, tweets count, number of tweets marked as favorites by the user, and the tweet average length. Other authors [85,86] use other network metrics such as centrality metrics since they analyze trust networks. We have not used them since our dataset is very sparse, based on the followed data collection strategy.
- Linguistic: the average count of bad words by tweet has been calculated using a publicly available list (https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words) of known bad words in Spanish and English (the two majority languages in our dataset). Then, we checked the presence of each tweet word in this list.
- Social: the Twitter network is unweighted and directed, with edges formed through the following action. Thus, we can analyze these edges separating them between the inner (followers) and outer edges (friends). The extracted features include features such as the count of friends and followers.
4. Prediction Model
5. Conclusions and Future Works
Conflicts of Interest
|API||Application Programming Interface|
|AUC||Area Under the Curve|
|C2C||Consumer to Consumer|
|ICT||Information and Communications technology|
|GLM||Generalized Linear Model|
|OSN||Online Social Network|
|P2P||Peer to Peer|
|RFR||Random Forest Regressor|
|SVM||Support Vector Machine|
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|Profile||Account creation date|
|Behavior||Tweets count, Favourites count, Most frequent tweeting hours, Tweets average length|
|Linguistic||Bad words ratio|
|Social||Times added to list, Avg. retweeted per tweet, Avg. favourited per tweet, Followers count, Friends count, Followers’ followers avg. count, Followers’ friends avg. count, Followers’ tweets avg. count, Friends’ friends avg. count, Friends’ tweets avg. count|
|Account creation date||-||-||2006-12-26||2017-03-14|
|Bad words ratio||0.02||0.02||0||0.46|
|Times added to list||9.76||42.45||0||2735|
|Avg. retweeted per tweet||1397.67||5039.60||0||365,745.75|
|Avg. favourited per tweet||0.49||9.77||0||906.60|
|Most frequent tweeting hours||15.24||6.83||0||23|
|Tweets average length||88.96||26.75||0||179.53|
|Followers’ followers avg. count||12,194.23||30,909.87||0||1,990,020|
|Followers’ friends avg.count||6945.60||12,969.62||0||441345.57|
|Followers’ tweets avg. count||7120.12||8148.27||0||327347.40|
|Friends’ followers avg. count||941,730.90||1,921,375.52||0||105,297,483|
|Friends’ friends avg.count||4323.63||8205.70||0||477,768.33|
|Friends’ tweets avg. count||17,643.89||12,186.34||0||391,207|
|Coef||Std Err||P > |z||
|Account creation date||2.957 × 10||4.77 × 10||<0.001|
|Tweets count||1.867 × 10||2.2 × 10||0.932|
|Bad words ratio||2.8823||1.246||0.021|
|Times added to list||−0.0013||0.001||0.350|
|Favourites count||8.7 × 10||2.59 × 10||0.737|
|Avg. retweeted per tweet||−4.387 × 10||7.57 × 10||0.562|
|Avg. favourited per tweet||0.0037||0.002||0.056|
|Most frequent tweeting hours||0.0052||0.005||0.259|
|Followers count||−4.28 × 10||3.63e-05||0.238|
|Friends count||6.774 × 10||3.84 × 10||0.078|
|Tweets average length||−0.0078||0.001||<0.001|
|Followers’ followers avg. count||−1.384 × 10||2.21 × 10||0.531|
|Followers’ friends avg. count||8.631 × 10||3.86 × 10||0.025|
|Followers’ tweets avg. count||−4.663 × 10||4.21 × 10||0.268|
|Friends’ followers avg. count||4.773 × 10||1.16 × 10||<0.001|
|Friends’ friends avg.count||1.236 × 10||3.46 × 10||0.721|
|Friends’ tweets avg. count||3.512 × 10||2.23 × 10||0.115|
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Prada, A.; Iglesias, C.A. Predicting Reputation in the Sharing Economy with Twitter Social Data. Appl. Sci. 2020, 10, 2881. https://doi.org/10.3390/app10082881
Prada A, Iglesias CA. Predicting Reputation in the Sharing Economy with Twitter Social Data. Applied Sciences. 2020; 10(8):2881. https://doi.org/10.3390/app10082881Chicago/Turabian Style
Prada, Antonio, and Carlos A. Iglesias. 2020. "Predicting Reputation in the Sharing Economy with Twitter Social Data" Applied Sciences 10, no. 8: 2881. https://doi.org/10.3390/app10082881