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The purpose of this study was to examine the orthodontic patient experience with braces compared to Invisalign® by means of a large-scale Twitter sentiment analysis. A custom data collection program was created to collect tweets containing the words “braces” or “Invisalign.” A hierarchal Naïve Bayes sentiment classifier was developed to sort the tweets into one of five categories: positive, negative, neutral, advertisement, or not applicable. Among the 419,363 tweets applicable to orthodontics collected, users posted significantly more positive tweets (61%) than negative tweets (39%) (p-value = ® tweets (p-value=0.4189). In conclusion, Twitter users express more positive than negative sentiment about orthodontic treatment with no significant difference in sentiment between braces and Invisalign® tweets.

Active investors are always trying to find new ways of systematically beating the market. Since the advent of social media, this has become one of the latest areas where investors are trying to find untapped information to exploit through a technique called sentiment analysis, which is the act of using automatic text processing to discern the opinions of social media users. The purpose of this study is to investigate the possibility of using the sentiment of tweets directed at specific companies to construct portfolios which generate abnormal returns by investing in companies based on the sentiment. To meet this purpose, we have collected company specific tweets for 40 companies from the Nasdaq 100 list. These 40 companies were selected using a simple random selection. To measure the sentiment tweets were downloaded from 2014 to 2016, giving us three years of data. From these tweets we extracted the sentiment using a sentiment program called SentiStrength. The sentiment score for every company was then calculated to a weekly average which we then used for our portfolio construction. The starting point for this study to try and explain the relationship between sentiment and stock returns was the following theories: The Efficient Market Hypothesis, Investor Attention and the Signaling Theory. Tweets act as signals which direct the attention of the investors to which stocks to purchase and, if our hypothesis is correct, this can be exploited to generate abnormal returns. To evaluate the performance of our portfolios the cumulative non-risk adjusted return for all of portfolios was initially calculated followed by calculations of the risk adjusted return by regressing both the Fama-French Three-Factor model and Carhart’s Four-Factor model with the returns for our different portfolios being the dependent variables. The results we obtained from these tests suggests that it might be possible to obtain abnormal returns by constructing portfolios based on the sentiment of tweets, using a few of the strategies tested in this study as no statistically significant negative results were found and a few significant positive results were found. Our conclusion is that the results seems to contradict the strong form of the Efficient Market Hypothesis on the Nasdaq 100 as the information contained in the sentiment of tweets seems to not be fully integrated within the share price. However, we cannot say this with confidence as the EMH is not a testable hypothesis and any test of the EMH is also a test of the models used to measure the efficiency of the market.

Master of Science / Department of Special Education, Counseling and Student Affairs / Doris Carroll / Social media is the use of online applications and websites to create and exchange user-generated content. These websites are becoming ever more popular with college aged students to connect with their peers, businesses, and areas of interest. These websites could be taken advantage of to provide new opportunities to engage students in campus life. This paper examines the concept of student engagement and the role of social media in engaging student with campus life. A brief overview of Facebook and Twitter, the two most popular social networks, is provided. This paper also reviews the limited body of research available on the impact of social media on student engagement. It is argued here that social media can be a positive influence on student engagement within the college campus and could lead to improvements in the way that higher education professional assist with student development. Due to the limited amount of academic research available, popular news sources as well as websites and blogs were examined to determine the most influential uses of social media, and this report makes recommendations for incorporating social media use into higher education. Social media allows higher education professionals to “meet students where they are” and provide for opportunities for engagement and student development. If the recommendations made in this report are implemented by student affairs professionals, they could be assessed for their impact on student engagement and development in the future.

Master of Science / Department of Journalism and Mass Communications / Joye C. Gordon / As technology advances in social media, crisis management professionals and researchers are charged with revamping or discovering new communication tools to address the dissemination crisis information. Social media provides a platform for open conversations, community, and connectedness among individuals and permits anyone to become the source of information during a time of crisis. Crisis news can be shared and reshared among millions of people without the need of a professional source, such as a journalist. A crisis may disrupt social order to an organization’s reputation and legitimacy, but a crisis also provides an opportunity for growth or renewal. Previous literature has analyzed crisis communication affects on organizational reputation through cases studies; however, there is lack of analysis in using an experimental design. Through an experiment with 207 undergraduate students, this study empirically evaluates the dissemination of crisis communication through Twitter and its effect on organizational communication. Using McLuhan’s (1967) concept of the medium is the message, this study highlights past findings, explicates types of crises, and focuses on the medium as a variable (not content of response) of interest to provide groundwork for an experimental inquiry into how the medium itself (as opposed to message content) impacts the efficacy of organizational crisis responses. A 2x3 experimental design with two research conditions- types of crisis: (1) intentional and (2) unintentional and source types: (1) organization (2) journalist, and (3) friend was used in this study. An online questionnaire was administered through an online survey service to approximately 2,000 undergraduates. Participants were randomized in one of six conditions based on the type of crisis (unintentional and intentional) and the source (organization/journalist/friend) of the message and directed to read an unintentional or intentional press release. Findings indicated that the perception of responsibility is a valid factor to consider during a possible crisis. Overall, as the previous studies have concluded, the organization is perceived as responsible for the crisis.

This thesis deals with social networks and their use in marketing. The aim of this work is the analysis of the possibility of using social networks in marketing and to define recommendations for the specific brand. The theoretical part deals with the definition of marketing, strategic marketing process and trends that currently affect them. The paper defined the concept of social media and social networks. The practical part deals with the utility of social networking and marketing benefits for their brands. They also described the practical use of the most important aspects of social networks in commercial communications. The work includes a case study, which is using social networks marketing, demonstrated in practice. Finally recommendations are defined to create a specific brand marketing strategies.

This article presents the findings of a corpus linguistic analysis of the hashtags #mansplaining, #manspreading, and #manterruption, three lexical blends which have recently found widespread use across a variety of online media platforms. Focusing on the social media and microblogging site Twitter, we analyze a corpus of over 20,000 tweets containing these hashtags to examine how discourses of gender politics and gender relations are represented on the site. More specifically, our analysis suggests that users include these hashtags in tweets to index their individual evaluations of, and assumptions about, "proper" gendered behavior. Consequently, their metadiscursive references to the respective phenomena reflect their beliefs of what constitutes appropriate (verbal) behavior and the extent to which gender is appropriated as a variable dictating this behavior. As such, this article adds to our knowledge of the ways in which gendered social practices become sites of contestation and how contemporary gender politics play out in social media sites.

Thesis advisor: Natalia Sarkisian / The recent proliferation of technologies to collect and analyze “Big Data” has changed the research landscape, making it easier for some to use unprecedented amounts of real-time data to guide decisions and build ‘knowledge.’ In the three articles of this dissertation, I examine what these changes reveal about the nature of expertise and the position of the researcher. In the first article, “Monopoly or Generosity? ‘Rock Stars’ of Big Data, Data Democrats, and the Role of Technologies in Systems of Expertise,” I challenge the claims of recent scholarship, which frames the monopoly of experts and the spread of systems of expertise as opposing forces. I analyze video recordings (N= 30) of the proceedings of two professional conferences about Big Data Analytics (BDA), and I identify distinct orientations towards BDA practice among presenters: (1) those who argue that BDA should be conducted by highly specialized “Rock Star” data experts, and (2) those who argue that access to BDA should be “democratized” to non-experts through the use of automated technology. While the “data democrats” ague that automating technology enhances the spread of the system of BDA expertise, they ignore the ways that it also enhances, and hides, the monopoly of the experts who designed the technology. In addition to its implications for practitioners of BDA, this work contributes to the sociology of expertise by demonstrating the importance of focusing on both monopoly and generosity in order to study power in systems of expertise, particularly those relying extensively on technology. Scholars have discussed several ways that the position of the researcher affects the production of knowledge. In “Distance Makes the Scholar Grow Fonder? The Relationship Between Analytical Distance and Critical Reflection on Methods in Big Data Analytics,” I pinpoint two types of researcher “distance” that have already been explored in the literature (experiential and interactional), and I identify a third type of distance—analytical distance—that has not been examined so far. Based on an empirical analysis of 113 articles that utilize Twitter data, I find that the analytical distance that authors maintain from the coding process is related to whether the authors include explicit critical reflections about their research in the article. Namely, articles in which the authors automate the coding process are significantly less likely to reflect on the reliability or validity of the study, even after controlling for factors such as article length and author’s discipline. These findings have implications for numerous research settings, from studies conducted by a team of scholars who delegate analytic tasks, to “big data” or “e-science” research that automates parts of the analytic process. Individuals who engage in self-tracking—collecting data about themselves or aspects of their lives for their own purposes—occupy a unique position as both researcher and subject. In the sociology of knowledge, previous research suggests that low experiential distance between researcher and subject can lead to more nuanced interpretations but also blind the researcher to his or her underlying assumptions. However, these prior studies of distance fail to explore what happens when the boundary between researcher and subject collapses in “N of one” studies. In “The Collapse of Experiential Distance and the Inescapable Ambiguity of Quantifying Selves,” I borrow from art and literary theories of grotesquerie—another instance of the collapse of boundaries—to examine the collapse of boundaries in self-tracking. Based on empirical analyses of video testimonies (N=102) and interviews (N=7) with members of the Quantified Self community of self-trackers, I find that ambiguity and multiplicity are integral facets of these data practices. I discuss the implications of these findings for the sociological study of researcher distance, and also the practical implications for the neoliberal turn that assigns responsibility to individuals to collect, analyze, and make the best use of personal data. / Thesis (PhD) — Boston College, 2015. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Sociology.

From the beginning, social sciences have looked to categorize people into groups that share common characteristics, to better serve the population, giving a distinguished treatment to each group. Applying this approach to the planning of business activities, we can better understand people’s needs, choosing the most favorable marketing strategies for each stratum of customers (saving effort in advertising and distribution) and maximize the level of satisfaction of each of market segment. Social Media is not a stranger to this principle: a correct segmentation will allow companies to avoid bringing content to people that are not part of their target audience, and to better respond to comments and complaints about their products and brands. However, some Social Media like Twitter still haven’t included demographic markers about their users within their marketing platforms, rendering decision-making difficult. In this paper, we demonstrate that it is possible to estimate important demographic information in Social Media by analyzing the tastes and preferences of the users (represented through the Twitter accounts they follow). We present four predictive models that allowed us to estimate the gender, age, socio-economic level and LATIR Lifestyle of a Twitter user. These models were trained using machine learning algorithms / Trabajo de investigación

In this paper, we present an analysis of the emotion-exchange patterns that arise from Twitter messages sent during emergency events. To this end, we performed a systematic structural analysis of the multiplex communication network that we derived from a data-set including more than 1.9 million tweets that have been sent during five recent shootings and terror events. In order to study the local communication structures that emerge as Twitter users directly exchange emotional messages, we propose the concept of emotion-exchangemotifs. Our findings suggest that emotion-exchange motifs which contain reciprocal edges (indicating online conversations) only emerge when users exchange messages that convey anger or fear, either in isolation or in any combination with another emotion. In contrast, the expression of sadness, disgust, surprise, as well as any positive emotion are rather characteristic for emotion-exchange motifs representing one-way communication patterns (instead of online conversations). Among other things, we also found that a higher structural similarity exists between pairs of network layers consisting of one high-arousal emotion and one low-arousal emotion, rather than pairs of network layers belonging to the same arousal dimension.