Consider a person trying to spread an important message on a social network. He/she can spend hours trying to craft the message. Does it actually matter? While there has been extensive prior work looking into predicting popularity of social-media content, the effect of wording per se has rarely been studied since it is often confounded with the popularity of the author and the topic. To control for these confounding factors, we take advantage of the surprising fact that there are many pairs of tweets containing the same url and written by the same user but employing different wording. Given such pairs, we ask: which version attracts more retweets? This turns out to be a more difficult task than predicting popular topics. Still, humans can answer this question better than chance (but far from perfectly), and the computational methods we develop can do better than both an average human and a strong competing method trained on non-controlled data.

The effect of wording on message propagation: Topic- and author-controlled natural experiments on Twitter

Looking for Information -- A Survey of Research on Information Seeking, Needs, and Behavior by Donald Case

Consider a person trying to spread an important message on a social network. He/she can spend hours trying to craft the message. Does it actually matter? While there has been extensive prior work looking into predicting popularity of socialmedia content, the effect of wording per se has rarely been studied since it is often confounded with the popularity of the author and the topic. To control for these confounding factors, we take advantage of the surprising fact that there are many pairs of tweets containing the same url and written by the same user but employing different wording. Given such pairs, we ask: which version attracts more retweets? This turns out to be a more difficult task than predicting popular topics. Still, humans can answer this question better than chance (but far from perfectly), and the computational methods we develop can do better than both an average human and a strong competing method trained on noncontrolled data.

/pdf/the-effect-of-wording-on-message-propagation-topic-and-84hce8uexc.pdf

Although analyzing user behavior within individual communities is an active and rich research domain, people usually interact with multiple communities both on- and off-line. How do users act in such multi-community environments? Although there are a host of intriguing aspects to this question, it has received much less attention in the research community in comparison to the intra-community case. In this paper, we examine three aspects of multi-community engagement: the sequence of communities that users post to, the language that users employ in those communities, and the feedback that users receive, using longitudinal posting behavior on Reddit as our main data source, and DBLP for auxiliary experiments. We also demonstrate the effectiveness of features drawn from these aspects in predicting users' future level of activity. One might expect that a user's trajectory mimics the "settling-down" process in real life: an initial exploration of sub-communities before settling down into a few niches. However, we find that the users in our data continually post in new communities; moreover, as time goes on, they post increasingly evenly among a more diverse set of smaller communities. Interestingly, it seems that users that eventually leave the community are "destined" to do so from the very beginning, in the sense of showing significantly different "wandering" patterns very early on in their trajectories; this finding has potentially important design implications for community maintainers. Our multi-community perspective also allows us to investigate the "situation vs. personality" debate from language usage across different communities.

All Who Wander: On the Prevalence and Characteristics of Multi-community Engagement

Does offline political segregation affect the filter bubble? An empirical analysis of information diversity for Dutch and Turkish Twitter users

Serendipity is a beneficial discovery that happens in an unexpected way. It has been found spectacularly valuable in various contexts, including scientific discoveries, acquisition of business, and recommender systems. Although never formally proved with large-scale behavioral analysis, it is believed by scientists and practitioners that serendipity is an important factor of positive user experience and increased user engagement. In this paper, we take the initiative to study the ubiquitous occurrence of serendipitious information diffusion and its effect in the context of microblogging communities. We refer to serendipity as unexpected relevance, then propose a principled statistical method to test the unexpectedness and the relevance of information received by a microblogging user, which identifies a serendipitous diffusion of information to the user. Our findings based on large-scale behavioral analysis reveal that there is a surprisingly strong presence of serendipitous information diffusion in retweeting, which accounts for more than 25% of retweets in both Twitter and Weibo. Upon the identification of serendipity, we are able to conduct observational analysis that reveals the benefit of serendipity to microblogging users. Results show that both the discovery and the provision of serendipity increase the level of user activities and social interactions, while the provision of serendipitous information also increases the influence of Twitter users.

/pdf/unexpected-relevance-an-empirical-study-of-serendipity-in-1cn8ul3bg0.pdf

Unexpected relevance: An empirical study of serendipity in retweets

Book-loan is the most important library service. Studying users' book-loan behavior patterns can help libraries to provide more proactive services. Based on users' book-loan history in a university library, we could build a book-borrowing network between users and books. Furthermore, users who borrow the same books are linked together. The users and links then form a co-borrowing network which can be regarded as a knowledge sharing network. Both the book-borrowing network and the co-borrowing network can be used to study users' bookloan behavior patterns. This paper presents a study in analyzing users' book-loan behaviors and mining knowledge dependency between schools and degrees in Peking University. The mining work is based on the book-borrowing network and its corresponding co-borrowing network. To the best of our knowledge, it is the first work to mine knowledge dependency in digital library domain.

Users' book-loan behaviors analysis and knowledge dependency mining

Serendipity is a beneficial discovery that happens in an unexpected way. It has been found spectacularly valuable in various contexts, including scientific discoveries, acquisition of business, and recommender systems. Although never formally proved with large-scale behavioral analysis, it is believed by scientists and practitioners that serendipity is an important factor of positive user experience and increased user engagement. In this paper, we take the initiative to study the ubiquitous occurrence of serendipitious information diffusion and its effect in the context of microblogging communities. We refer to serendipity as unexpected relevance, then propose a principled statistical method to test the unexpectedness and the relevance of information received by a microblogging user, which identifies a serendipitous diffusion of information to the user. Our findings based on large-scale behavioral analysis reveal that there is a surprisingly strong presence of serendipitous information diffusion in retweeting, which accounts for more than 25% of retweets in both Twitter and Weibo. Upon the identification of serendipity, we are able to conduct observational analysis that reveals the benefit of serendipity to microblogging users. Results show that both the discovery and provision of serendipity increase the level of user activities and social interactions, while the provision of serendipitous information also increases the influence of Twitter users.

Measuring the strength of ties has been a fundamental task for a long time. However, most of previous work treated it as classifying a tie as strong or weak and were not able to quantitatively estimate the strength, which limits their scope of contribution. To tackle the problem, through leveraging user similarities and social interactions, we propose a latent variable model to calculate a continuous value that measures the strength. By bringing real users as judge, we demonstrate that the proposed method can outperform previous methods. Further, we utilize it to measure the strength of ties among a large set of microblogging users, and conduct statistical analysis on triads. We find that comparing to other types of triads, the one with three significantly strong ties are more likely to be created, which verifies the theory of sociology.

Measuring Strength of Ties in Social Network

In a university library, students from different background are connected by co-borrowing behaviors which form a knowledge sharing network. This poster presents a novel idea to study the users' book-loan behavior patterns (knowledge sharing patterns) from the social network perspective which enable us to understand the patterns in both the macro-level and micro-level analysis.

Tao Sun

Papers

Unexpected relevance: An empirical study of serendipity in retweets

Users' book-loan behaviors analysis and knowledge dependency mining

Unexpected relevance: An empirical study of serendipity in retweets

Measuring Strength of Ties in Social Network

Analyzing user's book-loan behaviors in Peking university library from social network perspective