Local variation of collective attention in hashtag spike trains

In this paper, we propose a methodology quantifying temporal patterns of nonlinear hashtag time series. Our approach is based on an analogy between neuron spikes and hashtag diffusion. We adopt the local variation, originally developed to analyze local time delays in neuron spike trains. We show that the local variation successfully characterizes nonlinear features of hashtag spike trains such as burstiness and regularity. We apply this understanding in an extreme social event and are able to observe temporal evaluation of online collective attention of Twitter users to that event.</p

Local variation of hashtag spike trains and popularity in Twitter

We draw a parallel between hashtag time series and neuron spike trains. In each case, the process presents complex dynamic patterns including temporal correlations, burstiness, and all other types of nonstationarity. We propose the adoption of the so-called local variation in order to uncover salient dynamics, while properly detrending for the time-dependent features of a signal. The methodology is tested on both real and randomized hashtag spike trains, and identifies that popular hashtags present regular and so less bursty behavior, suggesting its potential use for predicting online popularity in social media.Comment: 7 pages, 7 figure

Temporal Pattern of Online Communication Spike Trains in Spreading a Scientific Rumor: How Often, Who Interacts with Whom?

We study complex time series (spike trains) of online user communication while spreading messages about the discovery of the Higgs boson in Twitter. We focus on online social interactions among users such as retweet, mention, and reply, and construct different types of active (performing an action) and passive (receiving an action) spike trains for each user. The spike trains are analyzed by means of local variation, to quantify the temporal behavior of active and passive users, as a function of their activity and popularity. We show that the active spike trains are bursty, independently of their activation frequency. For passive spike trains, in contrast, the local variation of popular users presents uncorrelated (Poisson random) dynamics. We further characterize the correlations of the local variation in different interactions. We obtain high values of correlation, and thus consistent temporal behavior, between retweets and mentions, but only for popular users, indicating that creating online attention suggests an alignment in the dynamics of the two interactions.Comment: A statistical data analysis & data mining on Social Dynamic Behavior, 9 pages and 7 figure

A hierarchical model of non-homogeneous Poisson processes for Twitter retweets

We present a hierarchical model of nonhomogeneous Poisson processes (NHPP) for information diffusion on online social media, in particular Twitter retweets. The retweets of each original tweet are modelled by a NHPP, for which the intensity function is a product of time-decaying components and another component that depends on the follower count of the original tweet author. The latter allows us to explain or predict the ultimate retweet count by a network centrality-related covariate. The inference algorithm enables the Bayes factor to be computed, to facilitate model selection. Finally, the model is applied to the retweet datasets of two hashtags. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplemen

Spotting Icebergs by the Tips: Rumor and Persuasion Campaign Detection in Social Media

Identifying different types of events in social media, i.e., collective online activities or posts, is critical for researchers who study data mining and online communication. However, the online activities of more than one billion social media users from around the world constitute an ocean of data that is hard to study and understand. In this dissertation, we study the problem of event detection with a focus on two important applications---rumor and persuasion campaign detection. Detecting events such as rumors and persuasion campaigns is particularly important for social media users and researchers. Events in social media spread and influence people much more quickly than traditional news media reporting. Viral spreading of specific events, such as rumors and persuasion campaigns, can cause substantial damage in online communities. Automatic detection of these can benefit analysts in many different research domains. In this thesis, we extend the existing research on social media event detection of online events such as rumors and persuasion campaigns. We conducted content analysis and found that the emergence and spreading of certain types of online events often result in similar user reactions. For example, some users will react to the spreading of a rumor by questioning its truth, even though most posts will not explicitly question it. These explicit questions serve as signals for detecting the underlying events. Our approach to detecting a given type of event first identifies the signals from the myriad of posts in the data corpus. We then use these signals to find the rest of the targeted events. Different types of events have different signals. As case studies, we analyze and identify the signals for rumors and persuasion campaigns, and we apply our proposed framework to detect these two types of events. We began by analyzing large-scale online activities in order to understand the relation between events and their signals. We focused on detecting and analyzing users' question-asking activities. We found that many social media users react to popular and fast-emerging memes by explicitly asking questions. Compared to other user activities, these questions are more likely to be correlated to bursty events and emergent information needs. We use some of our findings to detect trending rumors. We find that in the case of rumors, a common reaction regardless of the content of the rumor is to question the truth of the statement. We use these questioning activities as signals for detecting rumors. Our experimental results show that our rumor detector can effectively and efficiently detect social media rumors at an early stage. As in the case of rumors, the emergence and spreading of persuasion campaigns can result in similar reactions from the online audience. However, the explicit signals for detecting persuasion campaigns are not clearly understood and are difficult to label. We propose an algorithm that automatically learns these signals from data, by maximizing an objective that considers their key properties. We then use the learned signals in our proposed framework for detecting persuasion campaigns in social media. In our evaluation, we find that the learned signals can improve the performance of persuasion campaign detection compared to frameworks that use signals generated by alternative methods as well as those that do not use signals.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138726/1/zhezhao_1.pd

CSR, Big Data, and Accounting: Firms' Use of Social Media for CSR-Focused Reporting, Accountability, and Reputation Gain

The rise of Big Data, particularly social media, is engendering considerable disruptions in the ways in which firms and stakeholders communicate about firm-relevant issues. The effect of social media appears to be particularly strong in the domain of corporate social responsibility (CSR). This thesis presents three empirical studies on Fortune 200 firms use of social media to engage in CSR-related activities. All three studies rely on original 2014 data related to the 42 CSR-focused Twitter accounts maintained by the US-based Fortune 200 companies comprising 18,722 firm messages and 163,402 messages sent by members of the public. This thesis first examines the outcomes of firms social media-based CSR engagement, building a theoretical argument about the reputational benefits, or reputational capital, acquired by firms through the messages they send on social media. It then turns to an investigation of the publics discussion of the companies CSR activities; this second study relies on inductive analyses to build insights into the nature of the firm-centered CSR messages sent by members of the public, the nature of firms reactions to these public messages, and the relationship between the two. The third and final study refines and then empirically tests the causal model developed in the second study. Collectively, these three studies shed light on the nature of the micro-reporting and micro-accountability behaviors that appear to characterize firms CSR efforts on social media sites. The thesis concludes with a summary of the implications of these new behaviors for the accounting and CSR literatures

Uncovering Information Operations On Twitter Using Natural Language Processing And The Dynamic Wavelet Fingerprint

Information Operations (IO) are campaigns waged by covert, powerful entities to distort public discourse in a direction that is advantageous for them. It is the behaviors of the underlying networks that signal these campaigns in action, not the specific content they are posting. In this dissertation we introduce a social media analysis system that uncovers these behaviors by analyzing the specific post timings of underlying accounts and networks. The presented method first clusters tweets based on content using Natural Language Processing (NLP). Each of these clusters - referred to as topics - are plotted in time using the attached metadata for each tweet. These topic signals are then analyzed using the Dynamic Wavelet Fingerprint (DWFP), which creates binary images of each topic that describe localized behaviors in the topic\u27s propagation through Twitter. The features extracted from the DWFP and the underlying tweet metadata can be applied to various analyses. In this dissertation we present four applications of the presented method. First, we break down seven culturally significant tweet storms to identify characteristic, localized behavior that are common among and unique to each tweet storm. Next, we use the DWFP signal processing to identify bot accounts. Then this method is applied to a large dataset of tweets from the early weeks of the Covid-19 pandemic to identify densely connected communities, many of which display potential IO behaviors. Finally, this method is applied to a live-stream of Turkish tweets to identify coordinated networks working to push various agendas through a volatile time in Turkish politics

Using Social Media to Evaluate Public Acceptance of Infrastructure Projects

The deficit of infrastructure quality of the United States demands groundbreaking of more infrastructure projects. Despite the potential economic and social benefits brought by these projects, they could also negatively impact the community and the environment, which could in turn affect the implementation and operation of the projects. Therefore, measuring and monitoring public acceptance is critical to the success of infrastructure projects. However, current practices such as public hearings and opinion polls are slow and costly, hence are insufficient to provide satisfactory monitoring mechanism. Meanwhile, the development of state-of-the-art technologies such as social media and big data have provided people with unprecedented ways to express themselves. These platforms generate huge volumes of user-generated content, and have naturally become alternative sources of public opinion. This research proposes a framework and an analysis methodology to use big data from social media (e.g. the microblogging site Twitter) for project evaluation. The framework collects social media postings, analyzes public opinion towards infrastructure projects and builds multi-dimensional models around the big data. The interface and conceptual implementation of each component of the framework are discussed. This framework could be used as a supplement to traditional polls to provide a fast and cost-effective way for public opinion and project risk assessment. This research is followed by a case study applying the framework to a real-world infrastructure project to demonstrate the feasibility and comprehensiveness of the framework. The California High Speed Rail project is selected to be the object of study. It is an iconic and controversial large-scale infrastructure project that faced a lot of criticism, complaints and suggestions. Sentiment analysis, the most important type of analysis on the framework, is discussed concerning its application and implementation in the context of infrastructure projects. A public acceptance model for social media sentiment analysis is proposed and examined, and the best measurement of public acceptance is recommended. Moreover, the case study explores the driving force of the change in public acceptance: the social media events. Events are defined, evaluated, and an event influence quadrant is proposed to categorize and prioritize social media events. Furthermore, the individuals influencing the perceptions of these events, opinion leaders, are also modeled and identified. Three opinion leadership types are defined with top users in each type listed and discussed. A predictive model for opinion leader is also developed to identify opinion leaders using an a priori indicator. Finally, a user profiling model is established to describe social demographic characteristics of users, and each demographic feature is discussed in detail

Cultural Movement(s) and Counternarratives: The Rhetorics of Native Womxn Runners

This analysis demonstrates the complex ways that Native womxn runners mobilize the rhetoric they create around and through their running activity to challenge settler colonial, heteropatriarchal ideologies in favor of Indigenous lifeways; build upon cultural practices that include running and a wider spectrum of gender roles to enact more inclusive, modern Native identities; and lead intersectional advocacy efforts that center Native communities. Utilizing a Cultural Rhetorics, Indigenous Feminist, and Decolonial framework that recognizes Native womxn as experts in their own lived experiences, I have gathered Native womxn runners\u27 counternarratives from virtual spaces/social media to learn from their cultural, rhetorical (oral, written, digital, visual, embodied, and kinesthetic) practices via a process I call story gathering. Because I am non-Native, I sought to center Indigenous womxn\u27s voices by consulting Native womxn runners\u27 Instagram accounts or organizational websites; print and web-based articles that either quote or were written by these runners; and podcasts, televised interviews, or recorded workshops/panels for which they served as guests. As these sources highlight, Native womxn runners create their own coalitional counterpublics that continually enact cultural knowledge in context via discursive strategies that recognize Indigenous culture as diverse, inclusive, modern, living, vibrant, and embodied. As such, the runners\u27 social media presence and on/offline activism serve as rhetorical, cultural, and political acts. That is, they mobilize multiple modalities and rhetorics in culturally specific ways that have the potential to lead the mainstream (white) running industry toward greater inclusion and effect changes on a larger scale. I argue for a similar shift within Rhetoric and Composition, which still regards work by womxn of color as niche scholarship. To remedy this, the field must acknowledge Native womxn as not just knowledge keepers, but knowledge makers who should be better recognized and valued within our discipline regardless of their relation to the academy