Intermedia agenda setting (IAS) theory suggests that different news sources can influence each other's agenda. While this theory has been well-established in existing literature, whether it still holds in today's high-choice media environment, which includes news producers of different credibility and ideology dispositions, is an open question. Through two case studies--the 2016 and 2020 U.S. presidential elections--we show that media are still largely aligned, especially in broad topics they choose to cover, and that the level of alignment along the credibility dimension is comparable to that along the ideology dimension. Furthermore, we find that the coverage of the Republican candidate is better aligned across different media types than that of the Democratic candidate, and that media divergence has increased along both dimensions from 2016 to 2020. Finally, we demonstrate that high-credibility media still plays a dominant role in the IAS process, yet with a cautious warning of its declining IAS power for the Democratic candidate over the course of four years.

As the volume and complexity of forced movement continues to grow, there is an urgent need to use new data sources to better understand emerging crises. Organic sources, like social media and newspapers, can offer insights in near real time when administrative data are unavailable for timely and detailed analysis. However, in order to flexibly switch to different contexts, we need the ability to contextualize the drivers of movement for different locations and languages. Recent advances in natural language processing and specifically, neural machine translation, have shown impressive results on standard benchmark datasets for well-studied language pairs. However, the effectiveness of these models in a real-world scenario remains less known. To advance our understanding of real-world, contextual translation, we systematically study the performance of multiple widely used off-the-shelf machine translation tools using words associated with drivers of forced movement in both high- and low-resource languages. Our empirical results suggest significant variation between the performance of these machine translation tools in terms of accuracy and efficiency, highlighting a problem that must be faced by those conducting migration research using multilingual contexts. We conclude by suggesting strategies for obtaining reasonable translations from off-the-shelf language tools.

For some learning tasks, generating a large labeled data set is impractical. Demographic inference using social media data is one such task. While different strategies have been proposed to mitigate this challenge, including transfer learning, data augmentation, and data combination, they have not been explored for the task of user level demographic inference using social media data. This paper explores two of these strategies: data combination and transfer learning. First, we combine labeled training data from multiple data sets of similar size to understand when the combination is valuable and when it is not. Using data set distance, we quantify the relationship between our data sets to help explain the performance of the combination strategy. Then, we consider supervised transfer learning, where we pretrain a model on a larger labeled data set, fine-tune the model on smaller data sets, and incorporate regularization as part of the transfer learning process. We empirically show the strengths and limitations of the proposed techniques on multiple Twitter data sets.

While demographic attributes, such as age, gender, and location, have been extensively studied, most previous studies usually combine different sources of data, such as the user's biography, pictures, posts, and the user's network to obtain reasonable inference accuracies. However, it is not always practical to collect all those different forms of data. Therefore, in this paper, we consider methods for inferring age that only use Twitter posts (tweet text and emojis). We propose a hierarchical attention neural model that integrates independent linguistic knowledge gained from text and emojis when making a prediction. This hierarchical model is able to capture the intra-post relationship between these different post components, as well as the inter-post relationships of a user's posts. Our empirical evaluation using a data set generated from Wikidata demonstrates that our model achieves better performance than the state-of-the-art models, and still performs well when the number of posts per user is reduced in the training data set.

Worldwide displacement due to war and conflict is at all-time high. Unfortunately, determining if, when, and where people will move is a complex problem. This paper proposes integrating both publicly available organic data from social media and newspapers with more traditional indicators of forced migration to determine when and where people will move. We combine movement and organic variables with spatial and temporal variation within different Bayesian models and show the viability of our method using a case study involving displacement in Iraq. Our analysis shows that incorporating open-source generated conversation and event variables maintains or improves predictive accuracy over traditional variables alone. This work is an important step toward understanding how to leverage organic big data for societal--scale problems.

Meaningful leading indicators of mass movement are difficult to discover given the dearth of available data about involuntary movement. As a first step, we propose analyzing whether we can use the changing dynamics of newspaper content as one possible indirect indicator of such displacement. Specifically, we explore whether news media buzz correlates with patterns of migration in Iraq. We consider different methods for detecting buzz and empirically evaluate them on a corpus of 1.4 million articles.

Community detection is an important task for mining the structure and function of complex networks. Generally, there are several different kinds of nodes in a network which are cluster nodes densely connected within communities, as well as some special nodes like hubs bridging multiple communities and outliers marginally connected with a community. In addition, it has been shown that there is a hierarchical structure in complex networks with communities embedded within other communities. Therefore, a good algorithm is desirable to be able to not only detect hierarchical communities, but also identify hubs and outliers. In this paper, we propose a parameter-free hierarchical network clustering algorithm SHRINK by combining the advantages of density-based clustering and modularity optimization methods. Based on the structural connectivity information, the proposed algorithm can effectively reveal the embedded hierarchical community structure with multiresolution in large-scale weighted undirected networks, and identify hubs and outliers as well. Moreover, it overcomes the sensitive threshold problem of density-based clustering algorithms and the resolution limit possessed by other modularity-based methods. To illustrate our methodology, we conduct experiments with both real-world and synthetic datasets for community detection, and compare with many other baseline methods. Experimental results demonstrate that SHRINK achieves the best performance with consistent improvements.