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To what extent can we predict the structure of online conversation trees? We present a generative model to predict the size and evolution of threaded conversations on social media by combining machine learning algorithms. The model is evaluated using datasets that span two topical domains (cryptocurrency and cyber-security) and two platforms (Reddit and Twitter). We show that it is able to predict both macroscopic features of the final trees and near-future microscopic events with moderate accuracy. However, predicting the macroscopic structure of conversations does not guarantee an accurate reconstruction of their microscopic evolution. Our model’s limited performance in long-range predictions highlights the challenges faced by generative models due to the accumulation of errors.

Massive amounts of misinformation have been spreading over social media during the 2016 U.S. election season, causing wide public concern about our information ecosystem. How does fact checking compete with misinformation for user attentions? How do misinformation and fact checking differ in their spreading patterns? And what strategies are used by social bots to promote the spread of misinformation? In this paper, we address these research questions by analyzing datasets collected by Hoaxy. We answer the first question by conducting: 1) a survival analysis shows that about 70% of claims will be fact-checked in one week and about 800 tweets with claim links are posted during this period, on average and 2) a cross-correlation analysis shows that the sharing of fact-checking articles typically lags that of misinformation by about one day. Regarding the second question, we answer it from three points of view: 1) we find that false claims can become more popular than the corresponding debunking; 2) when looking at the distribution of types of tweets, we find that fact checking tends to spread in a conversational way; and 3) bot behavior analysis shows that the most active accounts sharing misinformation behave more like social bots. To better understand how social bots spread misinformation, we conduct case studies to answer the third question. By presenting strategies such as the production of a large number of original tweets, the alternating and hijacking of hashtags, and the injection of content into conversations, we demonstrate how social bots take advantages of the recommendation features of Twitter to amplify the spread of misinformation.

Efficient and reliable social bot classification is crucial for detecting information manipulation on social media. Despite rapid development, state-of-the-art bot detection models still face generalization and scalability challenges, which greatly limit their applications. In this paper we propose a framework that uses minimal account metadata, enabling efficient analysis that scales up to handle the full stream of public tweets of Twitter in real time. To ensure model accuracy, we build a rich collection of labeled datasets for training and validation. We deploy a strict validation system so that model performance on unseen datasets is also optimized, in addition to traditional cross-validation. We find that strategically selecting a subset of training data yields better model accuracy and generalization than exhaustively training on all available data. Thanks to the simplicity of the proposed model, its logic can be interpreted to provide insights into social bot characteristics., Comment: AAAI 2020

Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy filters public tweets that include links to unverified claims or fact-checking articles. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network., Comment: 28 pages, 11 figures, submitted to PLOS ONE

Social media and social networking platforms have flourished with the rapid development of mobile technology and the ubiquitous use of the Internet. As a result, memes, or pieces of information spreading from person to person, can be reshared among users quickly and gain huge popularity. As viral memes have tremendous social and economic impact, detecting these viral memes at their early stages of spread is a worthy, yet challenging problem. Here we review the literature on predicting viral memes, and present empirical results from Twitter and Tumblr datasets. We demonstrate how diffusion patterns of memes, in the context of network communities, play an important role in predicting virality. We show that it is feasible to obtain predictive features based on community structure even at the massive scales that common social media services need to process. Our results may not only enable practitioners to make predictions about meme diffusion, but also help researchers understand how and why different factors, in particular diffusion patterns in communities, affect online virality.

One of the challenges for recommender systems is that users struggle to accurately map their internal preferences to external measures of quality such as ratings. We study two methods for supporting the mapping process: (i) reminding the user of characteristics of items by providing personalized tags and (ii) relating rating decisions to prior rating decisions using exemplars. In our study, we introduce interfaces that provide these methods of support. We also present a set of methodologies to evaluate the efficacy of the new interfaces via a user experiment. Our results suggest that presenting exemplars during the rating process helps users rate more consistently, and increases the quality of the data.