Have You Heard?: How Gossip Flows Through Workplace Email

We spend a significant part of our lives chatting about other people. In other words, we all gossip. Although sometimes a contentious topic, various researchers have shown gossip to be fundamental to social life—from small groups to large, formal organizations. In this paper, we present the first study of gossip in a large CMC corpus. Adopting the Enron email dataset and natural language techniques, we arrive at four main findings. First, workplace gossip is common at all levels of the organizational hierarchy, with people most likely to gossip with their peers. Moreover, employees at the lowest level play a major role in circulating it. Second, gossip appears as often in personal exchanges as it does in formal business communication. Third, by deriving a power-law relation, we show that it is more likely for an email to contain gossip if targeted to a smaller audience. Finally, we explore the sentiment associated with gossip email, finding that gossip is in fact quite often negative: 2.7 times more frequent than positive gossip

Hubungan antara Kedengkian dan Kebosanan dengan Perilaku Bergosip pada Santri

This study aims to examine the relationship between envy and boredom with gossiping behavior in islamic students. These samples included 50 students male and 50 female students Pondok Pesantren An Nur Bantul, Jogjakarta, with characteristics such as age 17 years and over and memorize the Holly Qur’an. The sampling technique is random sampling method and the ordinal, measuring instruments used are gossiping behavior scale, the scale of envy and boredom scale. The methods of data analysis using multiple regression analysis. The results showed: 1) there is a significant relationship between envy and boredom together with the behavior of gossiping Islamic students. 2) there is a significant positive relationship between the envy with islamic students gossiping behavior. 3) there is a significant positive relationship between boredom with students gossiping behavior. This means that the variable envy and boredom can be used as a predictor for measuring the behavior of gossip. Effective contribution envy and boredom variables on the behavior of gossiping 28.9%. The remaining portion of 71.1% is the influence of other variables outside the research. In this study was also conducted Chow test to examine differences in behavior between male students gossiping and female students, the results showed no difference between male and female students in gossiping behavior. Thus, envy and boredom can be used as a predictor of the behavior of gossiping. This study found the presence of clinical indications associated with gossiping behavior, namely the discrepancy mind (cognitive disonance) on the perpetrators of gossip that causes mental stress and psychological discomfort. Envy is very strong trigger cardiovasculer risks and boredom lead to maladaptive behavior in delinquency among students like to break the rules cottage and procrastination. Therefore, focus group discussion (FGD) should be conducted on the Islamic Students

Understanding Bots on Social Media - An Application in Disaster Response

abstract: Social media has become a primary platform for real-time information sharing among users. News on social media spreads faster than traditional outlets and millions of users turn to this platform to receive the latest updates on major events especially disasters. Social media bridges the gap between the people who are affected by disasters, volunteers who offer contributions, and first responders. On the other hand, social media is a fertile ground for malicious users who purposefully disturb the relief processes facilitated on social media. These malicious users take advantage of social bots to overrun social media posts with fake images, rumors, and false information. This process causes distress and prevents actionable information from reaching the affected people. Social bots are automated accounts that are controlled by a malicious user and these bots have become prevalent on social media in recent years. In spite of existing efforts towards understanding and removing bots on social media, there are at least two drawbacks associated with the current bot detection algorithms: general-purpose bot detection methods are designed to be conservative and not label a user as a bot unless the algorithm is highly confident and they overlook the effect of users who are manipulated by bots and (unintentionally) spread their content. This study is trifold. First, I design a Machine Learning model that uses content and context of social media posts to detect actionable ones among them; it specifically focuses on tweets in which people ask for help after major disasters. Second, I focus on bots who can be a facilitator of malicious content spreading during disasters. I propose two methods for detecting bots on social media with a focus on the recall of the detection. Third, I study the characteristics of users who spread the content of malicious actors. These features have the potential to improve methods that detect malicious content such as fake news.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Biting the Hand That Feeds You: Employees' Reactions to Their Own Gossip about Highly (Un)Supportive Supervisors

This dissertation delves into the largely unexamined phenomena of workplace gossip. Drawing from deonance theory (Folger, 1998, 2001), I seek to explain the moral implications of gossip for the gossiper, specifically in terms of the moral emotions engagement in gossip elicits. I hypothesize the gossiper will experience shame and fear after gossiping about the supervisor. Furthermore, I examine the moderating role of the gossiper-gossipee relationship to assess the role interpersonal relationships play in relation to these emotional experiences. In an experience sample modeling field studies, I find that gossip fails to elicit shame and fear, but it does elicit the less intense emotions of guilt and anxiety. However, the data fail to support the moderation and mediated-moderation hypotheses.Business Administratio

Text Classification: Exploiting the Social Network

Within the context of social networks, existing methods for document classification tasks typically only capture textual semantics while ignoring the text’s metadata, e.g., the users who exchange emails and the communication networks they form. However, some work has shown that incorporating the social network information in addition to information from language is useful for various NLP applications, including sentiment analysis, inferring user attributes, and predicting interpersonal relations. In this thesis, we present empirical studies of incorporating social network information from the underlying communication graphs for various text classification tasks. We show different graph representations for different problems. Also, we introduce social network features extracted from these graphs. We use and extend graph embedding models for text classification. Our contributions are as follows. First, we have annotated large datasets of emails with fine-grained business and personal labels. Second, we propose graph representations for the social networks induced from documents and users and apply them on different text classification tasks. Third, we propose social network features extracted from these structures for documents and users. Fourth, we exploit different methods for modeling the social network of communication for four tasks: email classification into business and personal, overt display of power detection in emails, hierarchical power detection in emails, and Reddit post classification. Our main findings are: incorporating the social network information using our proposed methods improves the classification performance for all of the four tasks, and we beat the state-of-the-art graph embedding based model on the three tasks on email; additionally, for the fourth task (Reddit post classification), we argue that simple methods with the proper representation for the task can outperform a state-of-the-art generic model

Stylistics versus Statistics: A corpus linguistic approach to combining techniques in forensic authorship analysis using Enron emails

This thesis empirically investigates how a corpus linguistic approach can address the main theoretical and methodological challenges facing the field of forensic authorship analysis. Linguists approach the problem of questioned authorship from the theoretical position that each person has their own distinctive idiolect (Coulthard 2004: 431). However, the notion of idiolect has come under scrutiny in forensic linguistics over recent years for being too abstract to be of practical use (Grant 2010; Turell 2010). At the same time, two competing methodologies have developed in authorship analysis. On the one hand, there are qualitative stylistic approaches, and on the other there are statistical ‘stylometric’ techniques. This study uses a corpus of over 60,000 emails and 2.5 million words written by 176 employees of the former American company Enron to tackle these issues in the contexts of both authorship attribution (identifying authors using linguistic evidence) and author profiling (predicting authors’ social characteristics using linguistic evidence). Analyses reveal that even in shared communicative contexts, and when using very common lexical items, individual Enron employees produce distinctive collocation patterns and lexical co-selections. In turn, these idiolectal elements of linguistic output can be captured and quantified by word n-grams (strings of n words). An attribution experiment is performed using word n-grams to identify the authors of anonymised email samples. Results of the experiment are encouraging, and it is argued that the approach developed here offers a means by which stylistic and statistical techniques can complement each other. Finally, quantitative and qualitative analyses are combined in the sociolinguistic profiling of Enron employees by gender and occupation. Current author profiling research is exclusively statistical in nature. However, the findings here demonstrate that when statistical results are augmented by qualitative evidence, the complex relationship between language use and author identity can be more accurately observed

Computational and Causal Approaches on Social Media and Multimodal Sensing Data: Examining Wellbeing in Situated Contexts

A core aspect of our lives is often embedded in the communities we are situated in. The interconnectedness of our interactions and experiences intertwines our situated context with our wellbeing. A better understanding of wellbeing will help us devise proactive and tailored support strategies. However, existing methodologies to assess wellbeing suffer from limitations of scale and timeliness. These limitations are surmountable by social and ubiquitous technologies. Given its ubiquity and wide use, social media can be considered a “passive sensor” that can act as a complementary source of unobtrusive, real-time, and naturalistic data to infer wellbeing. This dissertation leverages social media in concert with multimodal sensing data, which facilitate analyzing dense and longitudinal behavior at scale. This work adopts machine learning, natural language, and causal inference analysis to infer wellbeing of individuals and collectives, particularly in situated communities, such as college campuses and workplaces. Before incorporating sensing modalities in practice, we need to account for confounds. One such confound that might impact behavior change is the phenomenon of “observer effect” --- that individuals may deviate from their typical or otherwise normal behavior because of the awareness of being “monitored”. I study this problem by leveraging the potential of longitudinal and historical behavioral data through social media. Focused on a multimodal sensing study, I conduct a causal study to measure observer effect in social media behavior, and explain the observations through existing theory in psychology and social science. The findings provide recommendations to correcting biases due to observer effect in social media sensing for human behavior and wellbeing. The novelties and contributions of this dissertation are four-fold. First, I use social media data that uniquely captures the behavior of situated communities. Second, I adopt theory-driven computational and causal methods to make conclusive research claims on wellbeing dynamics. Third, I address major challenges with methods to combine social media with multimodal sensing data for a comprehensive understanding of human behavior. Fourth, I draw interpretations and explanations of online-data-driven offline inferences. This dissertation situates the findings in an interdisciplinary context, including psychology and social science, and bears implications from theoretical, practical, design, methodological, and ethical perspectives catering to various stakeholders, including researchers, practitioners, and policymakers.Ph.D