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Genuine actors will react to the message they read and the messenger they encounter. We categorized these reactions into those related to affect and engagement (e.g., high arousal-specific emotions such as surprise or disgust) and those related to cognitive factors that influence belief, including factors that prompt individuals to engage in heuristic thinking. These affective and cognitive factors often interact in complex ways. We also categorized entertainment, humor, and intellectual engagement as inherently both related to affect and cognition. Believing in the content of the message is not necessary for individuals to share it online.

The literature identifies multiple motivations underlying individuals’ drive to share information on social media. We categorized these motivations using the psychological concept of needs that all humans have (i.e., need for impression management and enhancement, need for self-consistency and social identity, need for accuracy, and need for affiliation). Groups and individuals within a society may also have sociopolitical and economic motivations. Non-genuine actors are driven by their creators’ human motivations to share information. There may be individual differences in these motives, such that for some people, some of these motives are stronger than others. Individuals may also have different thresholds for sharing behavior, such that some share online more easily than others, based on how well they feel that information sharing on social media provides an outlet for these needs and how appropriate they feel their behavior is. These motivations are not mutually exclusive.

The act of sharing information online depends on the actor’s ability to share—that is, having access to social media platforms, being willing to use social media, and being familiar with relevant technology. Several social media affordances also play a role in whether people share online content. These affordances are both sociotechnical (i.e., the way people interact with technology) and practical (i.e., capabilities of specific platforms that allow users to create content, build networks, and share information).

This chapter tackled three major aspects of the social context: culture, narratives, and language; social comparison of beliefs and norms; and the structure and composition of one’s social network. These factors may have an impact not only on sharing behavior directly, but also on motivations and reactions to narratives and information. Culture, whether conceptualized as cross-national dimensions or culture-specific narratives, may impact how a particular piece of information is viewed, for instance. Comparisons between one’s own beliefs and that perceived to be held by one’s social group may influence how socially acceptable it is to state a particular belief. Echo chambers of people with the same belief may become more extreme. Indeed, these contextual factors also interact with each other, such that culture can be associated with structural differences in social networks. Although the reviewed studies have complex results with regards to social network ties, connections matter and are imperative to the spreading of information. This research also suggests that community structures, the composition of the community (homogeneous or diverse), perceptions of the specific audience, and network size are important factors in the spread, propagation and virality of information online.

Messages originate from a variety of sources. Social media users may create content, observe a message or narrative, or seek one out. What people view is influenced by what they search for and what is being shared already in their social networks.

Non-genuine actors, including social bots and sockpuppets, are automated or manipulated to post information and sentiment to various social media platforms. Though not all social bots and sockpuppets are nefarious, there is evidence that some of these non-genuine accounts—especially on Facebook and Twitter—are maliciously managed by hackers or state actors, to spread misinformation and spam. Trolls may or may not be automated, but are consciously or unconsciously divisive.

Despite years of research on vocabulary learning and teaching, relatively little is known about strategies for effective mastery of vocabulary in less commonly taught languages. The current study focuses on English native speakers studying Modern Standard Arabic to identify effective ways to present and learn new vocabulary using tasks varying in the degree of lexical context provided and the amount of cognitive effort needed to complete them. [ABSTRACT FROM AUTHOR]

This review summarizes evidence for the effectiveness of technology use in foreign language (FL) learning and teaching, with a focus on empirical studies that compare the use of newer technologies with more traditional methods or materials. The review of over 350 studies (including classroom-based technologies, individual study tools, network-based social computing, and mobile and portable devices) revealed that, in spite of an abundance of publications available on the topic of technology use in FL learning and teaching, evidence of efficacy is limited. However, strong support for the claim that technology made a measurable impact in FL learning came from studies on computer-assisted pronunciation training, in particular, automatic speech recognition (ASR). These studies demonstrated that ASR can facilitate the improvement of pronunciation and can provide feedback effectively. Additional studies provided strong support for the use of chat in FL learning. These studies showed that, with chat, both the amo...

The empirical study reported here investigated predictors of oral proficiency gain to the Advanced level resulting from immersion study abroad. The data consisted of 22 preimmersion Oral Proficiency Interviews (OPIs) of American students who participated in semester immersion programs in Moscow and St. Petersburg, and received a score of Intermediate High on the preimmersion OPI. On the basis of the students’ postprogram proficiency scores, the participants were grouped as learners who crossed the Advanced-level threshold (gainers) and as those who did not cross the threshold (nullgainers). This study used linguistic predictor variables identified in previous research (Brecht, Davidson, & Ginsberg, 1993) and introduced a new set of metalinguistic variables. An empirically based model constructed for the study included five variables that proved to be the best predictors for the Advanced-level threshold gain: linguistic (grammar, vocabulary, accuracy) and metalinguistic (self-corrected errors and sentence repair). The resulting findings yielded a robust model for predicting language outcomes of study abroad.

Sockpuppets are online identities controlled by a user or group of users to manipulate the dissemination of information in digital environments. This manipulation can distort computational assessments of public opinion in social media. Using Russian-language Twitter data from the Ukrainian crisis in 2014, we present a proof-of-concept model employing character n-gram methods to detect sockpuppets. Previous research has demonstrated that n-gram authorship attribution methods can capture lexical preferences, including grammatical and orthographic preferences, while also being less computationally intensive than grammatical or compression language models. Additionally, they can be applied to any language data irrespective of orthography. In this study, a Naive Bayes classifier was constructed using normalized frequencies of parsed character bigrams to contrast author bigram use. The created model illustrated that suspected sockpuppet accounts were less likely to be correctly classified, showing lower precision, recall, and f-measure rates than other accounts, as predicted.

Structural topic modeling (STM) is a recently introduced technique to model how the content of a collection of documents changes as a function of variables such as author identity or time of writing. We present two proof-of-concept applications of STM using Russian social media data. In our first study, we model how topics change over time, showing that STM can be used to detect significant events such as the downing of Malaysia Air Flight 17. In our second study, we model how topical content varies across a set of authors, showing that STM can be used to cluster Twitter users who are sympathetic to Ukraine versus Russia as well as to cluster accounts that are suspected to belong to the same individual (so-called “sockpuppets”). Structural topic modeling shows promise as a tool for analyzing social media data, a domain that has been largely ignored in the topic modeling literature.