Tracing Linguistic Relations in Winning and Losing Sides of Explicit Opposing Groups

Linguistic relations in oral conversations present how opinions are constructed and developed in a restricted time. The relations bond ideas, arguments, thoughts, and feelings, re-shape them during a speech, and finally build knowledge out of all information provided in the conversation. Speakers share a common interest to discuss. It is expected that each speaker's reply includes duplicated forms of words from previous speakers. However, linguistic adaptation is observed and evolves in a more complex path than just transferring slightly modified versions of common concepts. A conversation aiming a benefit at the end shows an emergent cooperation inducing the adaptation. Not only cooperation, but also competition drives the adaptation or an opposite scenario and one can capture the dynamic process by tracking how the concepts are linguistically linked. To uncover salient complex dynamic events in verbal communications, we attempt to discover self-organized linguistic relations hidden in a conversation with explicitly stated winners and losers. We examine open access data of the United States Supreme Court. Our understanding is crucial in big data research to guide how transition states in opinion mining and decision-making should be modeled and how this required knowledge to guide the model should be pinpointed, by filtering large amount of data.Comment: Full paper, Proceedings of FLAIRS-2017 (30th Florida Artificial Intelligence Research Society), Special Track, Artificial Intelligence for Big Social Data Analysi

Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs

Conversational participants tend to immediately and unconsciously adapt to each other's language styles: a speaker will even adjust the number of articles and other function words in their next utterance in response to the number in their partner's immediately preceding utterance. This striking level of coordination is thought to have arisen as a way to achieve social goals, such as gaining approval or emphasizing difference in status. But has the adaptation mechanism become so deeply embedded in the language-generation process as to become a reflex? We argue that fictional dialogs offer a way to study this question, since authors create the conversations but don't receive the social benefits (rather, the imagined characters do). Indeed, we find significant coordination across many families of function words in our large movie-script corpus. We also report suggestive preliminary findings on the effects of gender and other features; e.g., surprisingly, for articles, on average, characters adapt more to females than to males.Comment: data available at http://www.cs.cornell.edu/~cristian/movie

Loyalty in Online Communities

Loyalty is an essential component of multi-community engagement. When users have the choice to engage with a variety of different communities, they often become loyal to just one, focusing on that community at the expense of others. However, it is unclear how loyalty is manifested in user behavior, or whether loyalty is encouraged by certain community characteristics. In this paper we operationalize loyalty as a user-community relation: users loyal to a community consistently prefer it over all others; loyal communities retain their loyal users over time. By exploring this relation using a large dataset of discussion communities from Reddit, we reveal that loyalty is manifested in remarkably consistent behaviors across a wide spectrum of communities. Loyal users employ language that signals collective identity and engage with more esoteric, less popular content, indicating they may play a curational role in surfacing new material. Loyal communities have denser user-user interaction networks and lower rates of triadic closure, suggesting that community-level loyalty is associated with more cohesive interactions and less fragmentation into subgroups. We exploit these general patterns to predict future rates of loyalty. Our results show that a user's propensity to become loyal is apparent from their first interactions with a community, suggesting that some users are intrinsically loyal from the very beginning.Comment: Extended version of a paper appearing in the Proceedings of ICWSM 2017 (with the same title); please cite the official ICWSM versio

Antisocial Behavior in Online Discussion Communities

User contributions in the form of posts, comments, and votes are essential to the success of online communities. However, allowing user participation also invites undesirable behavior such as trolling. In this paper, we characterize antisocial behavior in three large online discussion communities by analyzing users who were banned from these communities. We find that such users tend to concentrate their efforts in a small number of threads, are more likely to post irrelevantly, and are more successful at garnering responses from other users. Studying the evolution of these users from the moment they join a community up to when they get banned, we find that not only do they write worse than other users over time, but they also become increasingly less tolerated by the community. Further, we discover that antisocial behavior is exacerbated when community feedback is overly harsh. Our analysis also reveals distinct groups of users with different levels of antisocial behavior that can change over time. We use these insights to identify antisocial users early on, a task of high practical importance to community maintainers.Comment: ICWSM 201

ANALISIS ATTITUDE DALAM PERUNDUNGAN SIBER PADA PELAJAR DI INDONESIA

Kejahatan kini tidak hanya tindakan yang berwujud pembunuhan, penyuapan, atau perbuatan kriminal lain. Bahasa yang digunakan untuk mencemooh, mengancam atau menghasut cukup untuk menjadi suatu tindak kejahatan. Kajian ini bertujuan untuk (1) menganalisis unsur kebahasaan perundungan siber menggunakan teori Appraisal dari Systemic Functional Linguistics (SFL) dalam aspek Attitude pada tuturan pelajar di media sosial, yaitu Instagram, Facebook, dan Twitter. Kajian ini merupakan kajian kualitatif dengan menerapkan metode deskriptif, yaitu menganalisis aspek kebahasaan pada 68 data tuturan. Tuturan tersebut diperoleh dari Badan Sandi dan Siber Negara (BSSN) melalui Program Inteligence Perception Analysis dan berpotensi mengandung muatan perundungan siber. Penelitian ini menemukan tiga jenis dominasi perundungan siber di Indonesia yakni (1) flaming atau saling mencemooh di media sosial, (2) harrassment atau memberikan komentar penghinaan, dan (3) impersonation atau berpura-pura menjadi seseorang untuk menyebarkan keburukan orang tersebut. Selain itu, hasil penelitian juga menunjukkan bahwa tuturan di media sosial memiliki bentuk-bentuk Attitude, yaitu Affect, Judgement, dan Appreciation. Tuturan perundungan didominasi oleh aspek Appreciation negatif dalam bentuk penilaian fisik terhadap objek perundungan. Penelitian ini merekomendasikan agar warganet perlu diberikan pemahaman agar lebih bijak dalam bertutur di dunia maya. Crime is now not only an act in the form of murder, bribery, or other criminal acts. The language used to bully, threaten or provoke is enough to be a crime. This study aims at analyzing the linguistic elements of cyber bullying using the Appraisal theory of Systemic Functional Linguistics (SFL). It focuses on the Attitude aspects of comments in social media, namely Instagram, Facebook, and Twitter. A descriptive qualitative method was employed in this study. Data containing cyberbullying in comment colums of the social media were acquired from National Cyber and Crypto Agency (Badan Siber dan Sandi Negara / BSSN) through the Intelligence Perception Analysis (IPA) Program. A number of 68 utterances, therefore, was obtained. The data were then selected using criteria based sampling.  The results of this study indicate that the most frequent cyberbullying types that appeared in the social media are flaming, harassment, and impersonation. In addition, they demonstrate that the data contain three aspects of Attitude, namely Affect, Judgment, and Appreciation. The most frequent aspect appearing in the data is Appreciation, especially negative reaction. This study suggests that a socialization of the responsible use of the internet can be carry out