As users’ social media feeds have become increasingly driven by algorithmically recommended content, there is a need to understand the impact these recommendations have on users. People in recovery from eating disorders (ED) may try to avoid content that features severely underweight bodies or that encourages disordered eating. However, if recommender systems show them this type of content anyway, it may impact their recovery or even lead to relapse. In this study, we take a two-pronged approach to understanding the intersection of recommender systems, ED content, and users in recovery. We performed a content analysis of tweets about recommended ED content and conducted a small-scale study on Pinterest to show that ED content is recommended in response to interaction with posts about ED recovery. We discuss the implications for responsible recommendation and harm prevention.

Abstract Due to their confidence and dominance, narcissistic leaders oftentimes can be perceived favorably by followers, in particular during times of uncertainty. In this study, we propose and examine the relationship between narcissistic leaders and followers who are prone to experience uncertainty intensely and frequently in general, namely highly anxious followers. We do so by applying machine learning algorithms to account for personality traits in a large sample of leaders and followers on Twitter. We find that highly anxious followers are more likely to interact with narcissistic leaders in general, and male narcissistic leaders in particular. Finally, we also examined these interactions in the context of highly popular leaders and found that as leaders become more popular, they begin to attract less anxious followers, regardless of leader gender. We interpret and discuss these findings in relation to previous work and outline limitations and future research recommendations based on our approach.

Alcoholism, also known as Alcohol Use Disorder (AUD), is a serious problem affecting millions of people worldwide. Recovery from AUD is known to be challenging and often leads to relapse at various points after enrolling in a rehabilitation program such as Alcoholics Anonymous (AA). In this work, we present a structured and linguistic approach using hinge-loss Markov random fields (HL-MRFs) to understand recovery and relapse from AUD using social media data. We evaluate our models on AA-attending users extracted from: (i) the Twitter social network and predict recovery at two different points—90 days and 1 year after the user joins AA, respectively, and (ii) the Reddit AA recovery forums and predict whether the participating user is currently sober. The two datasets present two facets of the same underlying problem of understanding recovery and relapse in AUD users. We flesh out different characteristics in both these datasets: (i) In the Twitter dataset, we focus on the social aspect of the users and the relationship with recovery and relapse, and (ii) in the Reddit dataset, we focus on modeling the linguistic topics and dependency structure to understand users’ recovery journey. We design a unified modeling framework using HL-MRFs that takes the different characteristics of both these platforms into account. Our experiments reveal that our structured and linguistic approach is helpful in predicting recovery in users in both these datasets. We perform extensive quantitative analysis of different groups of features and dependencies among them in both datasets. The interpretable and intuitive nature of our models and analysis is helpful in making meaningful predictions and can potentially be helpful in identifying and preventing relapse early.

This paper presents an observational study of lexical propagation across online social networking platforms. By focusing on the highly followed @dog_rates Twitter account, we explore how a popular account's unique style of language propagates outside of the account's immediate follower community within Twitter. Initial results show a strong relationship between the prevalence of this account's language-specific features and the account's followership and popularity. Expanding this research across platforms, we demonstrate consistency in these results outside Twitter, as the @dog_rates vernacular shows a similarly strong relationship between use on Reddit and the account's followership over time.

Alcoholism, also known as Alcohol Use Disorder (AUD), is a serious problem affecting millions of people worldwide. Recovery from AUD is known to be challenging and often leads to relapse at various points after enrolling in a rehabilitation program such as Alcoholics Anonymous (AA). In this work, we take a structured approach to understand recovery and relapse from AUD using social media data. To do so, we combine linguistic and psychological attributes of users with relational features that capture useful structure in the user interaction network. We evaluate our models on AA-attending users extracted from the Twitter social network and predict recovery at two different points---90 days and 1 year after the user joins AA, respectively. Our experiments reveal that our structured approach is helpful in predicting recovery in these users. We perform extensive quantitative analysis of different groups of features and dependencies among them. Our analysis sheds light on the role of each feature group and how they combine to predict recovery and relapse. Finally, we present a qualitative analysis of the different reasons behind users relapsing to AUD. Our models and analysis are helpful in making meaningful predictions in scenarios where only a subset of features are available and can potentially be helpful in identifying and preventing relapse early.

The microblogging platform, Twitter, has been an extremely valuable tool for politicians in sharing information, fostering broader communication to constituents, and promoting their political stances. This article follows up on previous research from 2009 on this subject. We reexamined tweets from the US Congress collected in early 2017. We found Congressional tweeting habits and content have changed very little in the last 8 years. Overall, they tended to use Twitter to pass along political information and links in addition to reporting on official and unofficial activities and meetings. We discuss future spaces for research that go beyond content analysis into issues of motivation, communication, and impact.

Social recommendation takes advantage of the influence of social relationships in decision making and the ready availability of social data through social networking systems. Trust relationships in particular can be exploited in such systems for rating prediction and recommendation, which has been shown to have the potential for improving the quality of the recommender and alleviating the issue of data sparsity, cold start, and adversarial attacks. An appropriate trust inference mechanism is necessary in extending the knowledge base of trust opinions and tackling the issue of limited trust information due to connection sparsity of social networks. In this work, we offer a new solution to trust inference in social networks to provide a better knowledge base for trust-aware recommender systems. We propose using a semiring framework as a nonlinear way to combine trust evidences for inferring trust, where trust relationship is model as 2-D vector containing both trust and certainty information. The trust propagation and aggregation rules, as the building blocks of our trust inference scheme, are based upon the properties of trust relationships. In our approach, both trust and distrust (i.e., positive and negative trust) are considered, and opinion conflict resolution is supported. We evaluate the proposed approach on real-world datasets, and show that our trust inference framework has high accuracy, and is capable of handling trust relationship in large networks. The inferred trust relationships can enlarge the knowledge base for trust information and improve the quality of trust-aware recommendation.

In recent years, cultural heritage institutions have increasingly used social tagging. To better understand the nature of these tags, we analyzed tags assigned to a collection of 100 images of art (provided by the steve.museum project) using subject matter categorization. Our results show that the majority of tags describe the people and objects in the image and are generic in nature. This contradicts prior subject matter analyses of queries, tags, and index terms of other image collections, suggesting that the nature of social tags largely depends on the type of collection and on user needs. This insight may help cultural heritage institutions improve their management and use of tags.

Personality is an important factor that influences people's decisions, actions, and tastes. While previous research has used surveys to establish a connection between personality and media preferences, to date there has been no research that connects these attributes to users' opinions of and use of recommender systems nor to their movie rating and viewing histories. In this paper, we present our results on the relationship between personality and users' movie preferences, and their opinions about, use of, and trust in recommender systems. Using surveys and analysis of system data for 73 Netflix users, we show correlations between personality and preferences for specific movie genres that replicate and extend previous results. Our most significant result is that the personality trait of Conscientious is consistently positively correlated with a higher opinion about the usefulness and trustworthiness of recommendations, self-reports of how frequently they were used, and ratings of recommended items. We discuss the implications these results have for evaluating and improving recommender systems.

In this paper, we examine to which degree behavioral measures can be used to predict personality. Personality is one factor that dictates people's propensity to trust and their relationships with others. In previous work, we have shown that personality can be predicted relatively accurately by analyzing social media profiles. We demonstrated this using public data from facebook profiles and text from Twitter streams. As social situations are crucial in the formation of one's personality, one's social behavior could be a strong indicator of her personality. Given most users of social media sites typically have a large number of friends and followers, considering only these aspects may not provide an accurate picture of personality. To overcome this problem, we develop a set of measures based on one's behavior towards her friends and followers. We introduce a number of measures that are based on the intensity and number of social interactions one has with friends along a number of dimensions such as reciprocity and priority. We analyze these features along with a set of features based on the textual analysis of the messages sent by the users. We show that behavioral features are very useful in determining personality and perform as well as textual features.

Social tags have become an important tool for improving access to online resources, particularly non-text media. With the dramatic growth of user-generated content, the importance of tags is likely to grow. However, while tagging behavior is well studied, the relationship between tagging behavior and features of the media being tagged is not well understood. In this paper, we examine the relationship between tagging behavior and image type. Through a lab-based study with 51 subjects and an analysis of an online dataset of image tags, we show that there are significant differences in the number, order, and type of tags that users assign based on their past experience with an image, the type of image being tagged, and other image features. We present these results and discuss the significant implications this work has for tag-based search algorithms, tag recommendation systems, and other interface issues.

Abstract Twitter is a microblogging and social networking service with millions of members and growing at a tremendous rate. With the buzz surrounding the service have come claims of its ability to transform the way people interact and share information and calls for public figures to start using the service. In this study, we are interested in the type of content that legislators are posting to the service, particularly by members of the United States Congress. We read and analyzed the content of over 6,000 posts from all members of Congress using the site. Our analysis shows that Congresspeople are primarily using Twitter to disperse information, particularly links to news articles about themselves and to their blog posts, and to report on their daily activities. These tend not to provide new insights into government or the legislative process or to improve transparency; rather, they are vehicles for self‐promotion. However, Twitter is also facilitating direct communication between Congresspeople and citizens, though this is a less popular activity. We report on our findings and analysis and discuss other uses of Twitter for legislators.

As the Web has shifted to an interactive environment where vast amounts of content is created by users, the question of whom to trust and what information to trust has become both more important and more difficult to answer. At the same time, social networks have become very popular with over a billion accounts shared across hundreds of networks. Social trust relationships, derived from social networks, are uniquely suited to speak to the quality of online information; recommender systems are designed to personalize, sort, aggregate, and highlight information. Merging social networks, trust, and recommender systems can improve the accuracy of recommendations and improve the user’s experience. In this tutorial, we will cover the use of social trust in recommender systems. Topics including the computation of trust in social networks, integration of trust into recommender systems, and a discussion of when trust offers benefits and the challenges it presents.

The World Wide Web has transformed into an environment where users both produce and consume information. In order to judge the validity of information, it is important to know how trustworthy its creator is. Since no individual can have direct knowledge of more than a small fraction of information authors, methods for inferring trust are needed. We propose a new trust inference scheme based on the idea that a trust network can be viewed as a random graph, and a chain of trust as a path in that graph. In addition to having an intuitive interpretation, our algorithm has several advantages, noteworthy among which is the creation of an inferred trust-metric space where the shorter the distance between two people, the higher their trust. Metric spaces have rigorous algorithms for clustering, visualization, and related problems, any of which is directly applicable to our results.

Online social networks, where users maintain lists of friends and express their preferences for items like movies, music, or books, are very popular. The Web-based nature of this information makes it ideal for use in a variety of intelligent systems that can take advantage of the users' social and personal data. For those systems to be effective, however, it is important to understand the relationship between social and personal preferences. In this work we investigate features of profile similarity and how those relate to the way users determine trust. Through a controlled study, we isolate several profile features beyond overall similarity that affect how much subjects trust hypothetical users. We then use data from FilmTrust, a real social network where users rate movies, and show that the profile features discovered in the experiment allow us to more accurately predict trust than when using only overall similarity. In this article, we present these experimental results and discuss the potential implications for using trust in user interfaces.