Wearing Many (Social) Hats: How Different are Your Different Social Network Personae?

This paper investigates when users create profiles in different social networks, whether they are redundant expressions of the same persona, or they are adapted to each platform. Using the personal webpages of 116,998 users on About.me, we identify and extract matched user profiles on several major social networks including Facebook, Twitter, LinkedIn, and Instagram. We find evidence for distinct site-specific norms, such as differences in the language used in the text of the profile self-description, and the kind of picture used as profile image. By learning a model that robustly identifies the platform given a user's profile image (0.657--0.829 AUC) or self-description (0.608--0.847 AUC), we confirm that users do adapt their behaviour to individual platforms in an identifiable and learnable manner. However, different genders and age groups adapt their behaviour differently from each other, and these differences are, in general, consistent across different platforms. We show that differences in social profile construction correspond to differences in how formal or informal the platform is.Comment: Accepted at the 11th International AAAI Conference on Web and Social Media (ICWSM17

Social Bootstrapping: How Pinterest and Last.fm Social Communities Benefit by Borrowing Links from Facebook

How does one develop a new online community that is highly engaging to each user and promotes social interaction? A number of websites offer friend-finding features that help users bootstrap social networks on the website by copying links from an established network like Facebook or Twitter. This paper quantifies the extent to which such social bootstrapping is effective in enhancing a social experience of the website. First, we develop a stylised analytical model that suggests that copying tends to produce a giant connected component (i.e., a connected community) quickly and preserves properties such as reciprocity and clustering, up to a linear multiplicative factor. Second, we use data from two websites, Pinterest and Last.fm, to empirically compare the subgraph of links copied from Facebook to links created natively. We find that the copied subgraph has a giant component, higher reciprocity and clustering, and confirm that the copied connections see higher social interactions. However, the need for copying diminishes as users become more active and influential. Such users tend to create links natively on the website, to users who are more similar to them than their Facebook friends. Our findings give new insights into understanding how bootstrapping from established social networks can help engage new users by enhancing social interactivity.Comment: Proc. 23rd International World Wide Web Conference (WWW), 201

Real-time Event Detection on Social Data Streams

Social networks are quickly becoming the primary medium for discussing what is happening around real-world events. The information that is generated on social platforms like Twitter can produce rich data streams for immediate insights into ongoing matters and the conversations around them. To tackle the problem of event detection, we model events as a list of clusters of trending entities over time. We describe a real-time system for discovering events that is modular in design and novel in scale and speed: it applies clustering on a large stream with millions of entities per minute and produces a dynamically updated set of events. In order to assess clustering methodologies, we build an evaluation dataset derived from a snapshot of the full Twitter Firehose and propose novel metrics for measuring clustering quality. Through experiments and system profiling, we highlight key results from the offline and online pipelines. Finally, we visualize a high profile event on Twitter to show the importance of modeling the evolution of events, especially those detected from social data streams.Comment: Accepted as a full paper at KDD 2019 on April 29, 201

Storia: Summarizing Social Media Content based on Narrative Theory using Crowdsourcing

People from all over the world use social media to share thoughts and opinions about events, and understanding what people say through these channels has been of increasing interest to researchers, journalists, and marketers alike. However, while automatically generated summaries enable people to consume large amounts of data efficiently, they do not provide the context needed for a viewer to fully understand an event. Narrative structure can provide templates for the order and manner in which this data is presented to create stories that are oriented around narrative elements rather than summaries made up of facts. In this paper, we use narrative theory as a framework for identifying the links between social media content. To do this, we designed crowdsourcing tasks to generate summaries of events based on commonly used narrative templates. In a controlled study, for certain types of events, people were more emotionally engaged with stories created with narrative structure and were also more likely to recommend them to others compared to summaries created without narrative structure

Pinning alone? A study of the role of social ties on pinterest

This paper seeks to answer the question of whether social ties are important on interest-driven social networks, by analysing 4-years of activities of 50,000 randomly sampled users on Pinterest, a social image discovery website. We find that a non-trivial number of users’ images are copied or repinned from strangers instead of friends, suggesting that social-based information exploration is not important. However, social interactions and social repins are critical for user retention: users interacting with friends are more likely to return Pinterest soon. These results suggest that the real role of social ties on interest-driven social networks is to enable bonding of users rather than seeking information

Predicting pinterest:Automating a distributed human computation

Everyday, millions of users save content items for future use on sites like Pinterest, by “pinning ” them onto carefully categorised personal pinboards, thereby creating personal taxonomies of the Web. This paper seeks to understand Pinterest as a distributed hu-man computation that categorises images from around the Web. We show that despite being categorised onto personal pinboards by in-dividual actions, there is a generally a global agreement in implic-itly assigning images into a coarse-grained global taxonomy of 32 categories, and furthermore, users tend to specialise in a handful of categories. By exploiting these characteristics, and augmenting with image-related features drawn from a state-of-the-art deep con-volutional neural network, we develop a cascade of predictors that together automate a large fraction of Pinterest actions. Our end-to-end model is able to both predict whether a user will repin an image onto her own pinboard, and also which pinboard she might choose, with an accuracy of 0.6

Sharing the Loves: Understanding the How and Why of Online Content Curation

This paper looks at how and why users categorise and curate content into collections online, using datasets containing nearly all the relevant activities from Pinterest.com during January 2013, and Last.fm in December 2012. In addition, a user survey of over 25 Pinterest and 250 Last.fm users is used to obtain insights into the motivations for content curation and corroborate results. The data reveal that curation tends to focus on items that may not rank highly in popularity and search rankings. Yet, curated items exhibit their own skewed popularity, with the top few items receiving most of the attention; indicative of a synchronised community. We distinguish structured curation by active categorisation from a more passive bookmarking by ‘liking ’ an item, and find the former more prevalent for popularly curated items. Likes, however, are initially accumulated at a faster pace. Finally, we study the social value of content curation and show that curators attract more followers with consistent activity, and diversity of interests. Interestingly, our user study indicates a divided opinion on the relevance of the social network