Showing papers on "Social media published in 2011"

Social Media? Get Serious! Understanding the Functional Building Blocks of Social Media

Abstract: Traditionally, consumers used the Internet to simply expend content: they read it, they watched it, and they used it to buy products and services. Increasingly, however, consumers are utilizing platforms – such as content sharing sites, blogs, social networking, and wikis – to create, modify, share, and discuss Internet content. This represents the social media phenomenon, which can now significantly impact a firm’s reputation, sales, and even survival. Yet, many executives eschew or ignore this form of media because they don’t understand what it is, the various forms it can take, and how to engage with it and learn. In response, we present a framework that defines social media by using seven functional building blocks: identity, conversations, sharing, presence, relationships, reputation, and groups. As different social media activities are defined by the extent to which they focus on some or all of these blocks, we explain the implications that each block can have for how firms should engage with social media. To conclude, we present a number of recommendations regarding how firms should develop strategies for monitoring, understanding, and responding to different social media activities.

I Tweet Honestly, I Tweet Passionately: Twitter Users, Context Collapse, and the Imagined Audience

Abstract: Social media technologies collapse multiple audiences into single contexts, making it difficult for people to use the same techniques online that they do to handle multiplicity in face-to-face conversation. This article investigates how content producers navigate ‘imagined audiences’ on Twitter. We talked with participants who have different types of followings to understand their techniques, including targeting different audiences, concealing subjects, and maintaining authenticity. Some techniques of audience management resemble the practices of ‘micro-celebrity’ and personal branding, both strategic self-commodification. Our model of the networked audience assumes a many-to-many communication through which individuals conceptualize an imagined audience evoked through their tweets.

Determinants of consumer engagement in electronic word-of-mouth in social networking sites

Abstract: As more and more marketers incorporate social media as an integral part of the promotional mix, rigorous investigation of the determinants that impact consumers’ engagement in eWOM via social networks is becoming critical. Given the social and communal characteristics of social networking sites (SNSs) such as Facebook, MySpace and Friendster, this study examines how social relationship factors relate to eWOM transmitted via online social websites. Specifically, a conceptual model that identifies tie strength, homophily, trust, normative and informational interpersonal influence as an important antecedent to eWOM behaviour in SNSs was developed and tested. The results confirm that tie strength, trust, normative and informational influence are positively associated with users’ overall eWOM behaviour, whereas a negative relationship was found with regard to homophily. This study suggests that product-focused eWOM in SNSs is a unique phenomenon with important social implications. The implications for research...

Clinical Report—The Impact of Social Media on Children, Adolescents, and Families

Abstract: social media Web sites is among the most common activity of today's children and adolescents. Any Web site that allows social inter- action is considered a social media site, including social networking sites such as Facebook, MySpace, and Twitter; gaming sites and virtual worlds such as Club Penguin, Second Life, and the Sims; video sites such as YouTube; and blogs. Such sites offer today's youth a portal for entertainment and communication and have grown exponentially in recent years. For this reason, it is important that parents become aware of the nature of social media sites, given that not all of them are healthy environments for children and adolescents. Pediatricians are in a unique position to help families understand these sites and to encourage healthy use and urge parents to monitor for potential prob- lems with cyberbullying, "Facebook depression," sexting, and exposure to inappropriate content. Pediatrics 2011;127:800-804 SOCIAL MEDIA USE BY TWEENS AND TEENS Engaging in various forms of social media is a routine activity that research has shown to benefit children and adolescents by enhancing communication, social connection, and even technical skills. 1 Social media sites such as Facebook and MySpace offer multiple daily oppor- tunities for connecting with friends, classmates, and people with shared interests. During the last 5 years, the number of preadoles- cents and adolescents using such sites has increased dramatically. According to a recent poll, 22% of teenagers log on to their favorite social media site more than 10 times a day, and more than half of adolescents log on to a social media site more than once a day. 2 Seventy-five percent of teenagers now own cell phones, and 25% use them for social media, 54% use them for texting, and 24% use them for instant messaging. 3 Thus, a large part of this generation's social and

The Effect of Twitter on College Student Engagement and Grades

Abstract: Despite the widespread use of social media by students and its increased use by instructors, very little empirical evidence is available concerning the impact of social media use on student learning and engagement. This paper describes our semester-long experimental study to determine if using Twitter – the microblogging and social networking platform most amenable to ongoing, public dialogue – for educationally relevant purposes can impact college student engagement and grades. A total of 125 students taking a first year seminar course for pre-health professional majors participated in this study (70 in the experimental group and 55 in the control group). With the experimental group, Twitter was used for various types of academic and co-curricular discussions. Engagement was quantified by using a 19-item scale based on the National Survey of Student Engagement. To assess differences in engagement and grades, we used mixed effects analysis of variance (ANOVA) models, with class sections nested within treatment groups. We also conducted content analyses of samples of Twitter exchanges. The ANOVA results showed that the experimental group had a significantly greater increase in engagement than the control group, as well as higher semester grade point averages. Analyses of Twitter communications showed that students and faculty were both highly engaged in the learning process in ways that transcended traditional classroom activities. This study provides experimental evidence that Twitter can be used as an educational tool to help engage students and to mobilize faculty into a more active and participatory role.

Political Polarization on Twitter

Abstract: In this study we investigate how social media shape the networked public sphere and facilitate communication between communities with different political orientations. We examine two networks of political communication on Twitter, comprised of more than 250,000 tweets from the six weeks leading up to the 2010 U.S. congressional midterm elections. Using a combination of network clustering algorithms and manually-annotated data we demonstrate that the network of political retweets exhibits a highly segregated partisan structure, with extremely limited connectivity between left- and right-leaning users. Surprisingly this is not the case for the user-to-user mention network, which is dominated by a single politically heterogeneous cluster of users in which ideologically-opposed individuals interact at a much higher rate compared to the network of retweets. To explain the distinct topologies of the retweet and mention networks we conjecture that politically motivated individuals provoke interaction by injecting partisan content into information streams whose primary audience consists of ideologically-opposed users. We conclude with statistical evidence in support of this hypothesis.

Comparing twitter and traditional media using topic models

Abstract: Twitter as a new form of social media can potentially contain much useful information, but content analysis on Twitter has not been well studied. In particular, it is not clear whether as an information source Twitter can be simply regarded as a faster news feed that covers mostly the same information as traditional news media. In This paper we empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling. We use a Twitter-LDA model to discover topics from a representative sample of the entire Twitter. We then use text mining techniques to compare these Twitter topics with topics from New York Times, taking into consideration topic categories and types. We also study the relation between the proportions of opinionated tweets and retweets and topic categories and types. Our comparisons show interesting and useful findings for downstream IR or DM applications.

Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter

Abstract: There is a widespread intuitive sense that different kinds of information spread differently on-line, but it has been difficult to evaluate this question quantitatively since it requires a setting where many different kinds of information spread in a shared environment. Here we study this issue on Twitter, analyzing the ways in which tokens known as hashtags spread on a network defined by the interactions among Twitter users. We find significant variation in the ways that widely-used hashtags on different topics spread.Our results show that this variation is not attributable simply to differences in "stickiness," the probability of adoption based on one or more exposures, but also to a quantity that could be viewed as a kind of "persistence" - the relative extent to which repeated exposures to a hashtag continue to have significant marginal effects. We find that hashtags on politically controversial topics are particularly persistent, with repeated exposures continuing to have unusually large marginal effects on adoption; this provides, to our knowledge, the first large-scale validation of the "complex contagion" principle from sociology, which posits that repeated exposures to an idea are particularly crucial when the idea is in some way controversial or contentious. Among other findings, we discover that hashtags representing the natural analogues of Twitter idioms and neologisms are particularly non-persistent, with the effect of multiple exposures decaying rapidly relative to the first exposure.We also study the subgraph structure of the initial adopters for different widely-adopted hashtags, again finding structural differences across topics. We develop simulation-based and generative models to analyze how the adoption dynamics interact with the network structure of the early adopters on which a hashtag spreads.

Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments

Abstract: We address the problem of part-of-speech tagging for English data from the popular micro-blogging service Twitter. We develop a tagset, annotate data, develop features, and report tagging results nearing 90% accuracy. The data and tools have been made available to the research community with the goal of enabling richer text analysis of Twitter and related social media data sets.

You Are What You Tweet: Analyzing Twitter for Public Health

Abstract: Analyzing user messages in social media can measure different population characteristics, including public health measures. For example, recent work has correlated Twitter messages with influenza rates in the United States; but this has largely been the extent of mining Twitter for public health. In this work, we consider a broader range of public health applications for Twitter. We apply the recently introduced Ailment Topic Aspect Model to over one and a half million health related tweets and discover mentions of over a dozen ailments, including allergies, obesity and insomnia. We introduce extensions to incorporate prior knowledge into this model and apply it to several tasks: tracking illnesses over times (syndromic surveillance), measuring behavioral risk factors, localizing illnesses by geographic region, and analyzing symptoms and medication usage. We show quantitative correlations with public health data and qualitative evaluations of model output. Our results suggest that Twitter has broad applicability for public health research.

The political power of social media: Technology, the public sphere, and political change

Abstract: On January 17, 2001, during the impeachment trial of Philippine President Joseph Estrada, loyalists in the Philippine Congress voted to set aside key evidence against him. Less than two hours after the decision was announced, thousands of Filipinos, angry that their corrupt president might be let off the hook, converged on Epifanio de los Santos Avenue, a major crossroads in Manila. The protest was arranged, in part, by forwarded text messages reading, "Go 2 EDSA. Wear blk." The crowd quickly swelled, and in the next few days, over a million people arrived, choking traffic in downtown Manila.

Introducing COBRAs: exploring motivations for brand-related social media use

Abstract: The article examines the use of social media by Internet users related to advertising and marketing, called "consumers' online brand-related activities (COBRA)." Interviews are conducted with such Internet users through instant messaging as to their motivations for engaging with brands and brand name products through social media. It was found that a desire for information, a desire for entertainment and the possibility of reward were the primary motivations for COBRA activity by Internet users, with entertainment being the primary motivation for the generation of brand-related social media content.

Emergency knowledge management and social media technologies: A case study of the 2010 Haitian earthquake

Abstract: The US response to the 2010 Haiti Earthquake was a large effort coordinated by three major agencies that worked in tandem with the Government of Haiti, the United Nations, and many countries from around the globe. Managing this response effort was a complex undertaking that relied extensively on knowledge management systems (KMS). For the first time, however, US government agencies employed social media technologies such as wikis and collaborative workspaces as the main knowledge sharing mechanisms. In this research we present a case study developed through action research of how these social media technologies were used, what influences they made on knowledge sharing, reuse, and decision-making, and how knowledge was effectively (and at times ineffectively) maintained in these systems. First-hand knowledge of the response is used, offering strategies for future deployment of social media and important research questions that remain regarding social media as knowledge management systems, particularly for disaster and emergency management.

To See and Be Seen: Celebrity Practice on Twitter

Abstract: Social media technologies let people connect by creating and sharing content. We examine the use of Twitter by famous people to conceptualize celebrity as a practice. On Twitter, celebrity is practiced through the appearance and performance of ‘backstage’ access. Celebrity practitioners reveal what appears to be personal information to create a sense of intimacy between participant and follower, publicly acknowledge fans, and use language and cultural references to create affiliations with followers. Interactions with other celebrity practitioners and personalities give the impression of candid, uncensored looks at the people behind the personas. But the indeterminate ‘authenticity’ of these performances appeals to some audiences, who enjoy the game playing intrinsic to gossip consumption. While celebrity practice is theoretically open to all, it is not an equalizer or democratizing discourse. Indeed, in order to successfully practice celebrity, fans must recognize the power differentials intrinsic to the...

Event Detection in Twitter

Abstract: Twitter, as a form of social media, is fast emerging in recent years. Users are using Twitter to report real-life events. This paper focuses on detecting those events by analyzing the text stream in Twitter. Although event detection has long been a research topic, the characteristics of Twitter make it a non-trivial task. Tweets reporting such events are usually overwhelmed by high flood of meaningless “babbles”. Moreover, event detection algorithm needs to be scalable given the sheer amount of tweets. This paper attempts to tackle these challenges with EDCoW (Event Detection with Clustering of Wavelet-based Signals). EDCoW builds signals for individual words by applying wavelet analysis on the frequencybased raw signals of the words. It then filters away the trivial words by looking at their corresponding signal autocorrelations. The remaining words are then clustered to form events with a modularity-based graph partitioning technique. Experimental results show promising result of EDCoW.

Beyond Trending Topics: Real-World Event Identification on Twitter

Abstract: User-contributed messages on social media sites such as Twitter have emerged aspowerful, real-time means of information sharing on the Web. These short messages tend to reflect a variety of events in real time, making Twitter particularly well suited as a source of real-time event content. In this paper, we explore approaches for analyzing the stream of Twitter messages to distinguish between messages about real-world events andnon-event messages. Our approach relies on a rich family of aggregatestatistics of topically similar message clusters. Large-scale experiments over millions of Twitter messages show the effectiveness of our approach for surfacing real-world event content on Twitter.

Introduction to the Special Issue Social Commerce: A Research Framework for Social Commerce

Abstract: The increased popularity of social networking sites, such as Linkedln, Facebook, and Twitter, has opened opportunities for new business models for electronic commerce, often referred to as social commerce. Social commerce involves using Web 2.0 social media technologies and infrastructure to support online interactions and user contributions to assist in the acquisition of products and services. Social media technologies not only provide a new platform for entrepreneurs to innovate but also raise a variety of new issues for e-commerce researchers that require the development of new theories. This could become one of the most challenging research arenas in the coming decade. The purpose of this introduction is to present a framework that integrates several elements in social commerce research and to summarize the papers included in this special issue. The framework includes six key elements for classifying social commerce research: research theme, social media, commercial activities, underlying theories, outcomes, and research methods. The proposed framework is valuable in defining the scope and identifying potential research issues in social commerce. We also explain how the papers included in this special issue fit into the proposed research framework.

Opening Closed Regimes: What was the Role of Social Media during the Arab Spring?

Abstract: Social media played a central role in shaping political debates in the Arab Spring. A spike in online revolutionary conversations often preceded major events on the ground. Social media helped spread democratic ideas across international borders.No one could have predicted that Mohammed Bouazizi would play a role in unleashing a wave of protest for democracy in the Arab world. Yet, after the young vegetable merchant stepped in front of a municipal building in Tunisia and set himself on fire in protest of the government on December 17, 2010, democratic fervor spread across North Africa and the Middle East.Governments in Tunisia and Egypt soon fell, civil war broke out in Libya, and protestors took to the streets in Algeria, Morocco, Syria, Yemen and elsewhere. The Arab Spring had many causes. One of these sources was social media and its power to put a human face on political oppression. Bouazizi’s self-immolation was one of several stories told and retold on Facebook, Twitter, and YouTube in ways that inspired dissidents to organize protests, criticize their governments, and spread ideas about democracy. Until now, most of what we have known about the role of social media in the Arab Spring has been anecdotal.Focused mainly on Tunisia and Egypt, this research included creating a unique database of information collected from Facebook, Twitter, and YouTube. The research also included creating maps of important Egyptian political websites, examining political conversations in the Tunisian blogosphere, analyzing more than 3 million Tweets based on keywords used, and tracking which countries thousands of individuals tweeted from during the revolutions. The result is that for the first time we have evidence confirming social media’s critical role in the Arab Spring.

Discriminating Gender on Twitter

Abstract: Accurate prediction of demographic attributes from social media and other informal online content is valuable for marketing, personalization, and legal investigation. This paper describes the construction of a large, multilingual dataset labeled with gender, and investigates statistical models for determining the gender of uncharacterized Twitter users. We explore several different classifier types on this dataset. We show the degree to which classifier accuracy varies based on tweet volumes as well as when various kinds of profile metadata are included in the models. We also perform a large-scale human assessment using Amazon Mechanical Turk. Our methods significantly out-perform both baseline models and almost all humans on the same task.

Predicting the Political Alignment of Twitter Users

Abstract: The widespread adoption of social media for political communication creates unprecedented opportunities to monitor the opinions of large numbers of politically active individuals in real time. However, without a way to distinguish between users of opposing political alignments, conflicting signals at the individual level may, in the aggregate, obscure partisan differences in opinion that are important to political strategy. In this article we describe several methods for predicting the political alignment of Twitter users based on the content and structure of their political communication in the run-up to the 2010 U.S. midterm elections. Using a data set of 1,000 manually-annotated individuals, we find that a support vector machine (SVM) trained on hash tag metadata outperforms an SVM trained on the full text of users' tweets, yielding predictions of political affiliations with 91% accuracy. Applying latent semantic analysis to the content of users' tweets we identify hidden structure in the data strongly associated with political affiliation, but do not find that topic detection improves prediction performance. All of these content-based methods are outperformed by a classifier based on the segregated community structure of political information diffusion networks (95% accuracy). We conclude with a practical application of this machinery to web-based political advertising, and outline several approaches to public opinion monitoring based on the techniques developed herein.

Predicting popular messages in Twitter

Abstract: Social network services have become a viable source of information for users. In Twitter, information deemed important by the community propagates through retweets. Studying the characteristics of such popular messages is important for a number of tasks, such as breaking news detection, personalized message recommendation, viral marketing and others. This paper investigates the problem of predicting the popularity of messages as measured by the number of future retweets and sheds some light on what kinds of factors influence information propagation in Twitter. We formulate the task into a classification problem and study two of its variants by investigating a wide spectrum of features based on the content of the messages, temporal information, metadata of messages and users, as well as structural properties of the users' social graph on a large scale dataset. We show that our method can successfully predict messages which will attract thousands of retweets with good performance.

Friends, Fans, and Followers: Do Ads Work on Social Networks?

Abstract: Social-networking sites (SNS) such as Facebook and Twitter are growing in both popularity and number of users For advertisers and the sites themselves, it is crucial that users accept advertising as a component of the SNS Anecdotal evidence indicates that social-networking advertising (SNA) can be effective when users accept it, but the perception of excessive commercialization may lead to user abandonment Empirical support for these propositions, however, is lacking Based on media uses and gratification theory, the authors propose and empirically test a model of content-related, structural, and socialization factors that affect users9 attitudes toward SNA

A Machine Learning Approach to Twitter User Classification

Abstract: This paper addresses the task of user classification in social media, with an application to Twitter. We automatically infer the values of user attributes such as political orientation or ethnicity by leveraging observable information such as the user behavior, network structure and the linguistic content of the user’s Twitter feed. We employ a machine learning approach which relies on a comprehensive set of features derived from such user information. We report encouraging experimental results on 3 tasks with different characteristics: political affiliation detection, ethnicity identification and detecting affinity for a particular business. Finally, our analysis shows that rich linguistic features prove consistently valuable across the 3 tasks and show great promise for additional user classification needs.