Twitter Interactions Research Articles

Hypertextuality and Social Media

Texts and conversations are central to the constitution of organizations. Through the use of social media technologies, organizational members and nonorganizational members alike have the capacity to author organizational texts that co-constitute an organization as an entity with a specific identity in a situational space and time. The implications of this ability are underexplored. This study focused on how two organizations used the social media technology Twitter to interact with their constituents. The article adopts communication-centered and sociomateriality perspectives to illustrate how Twitter interactions (hashtags) become hypertexts that simultaneously coproduce an organizational actor and act as a pastiche of the organization (i.e., a vehicle of contestation for the specific identity they were designed to bring into existence). The findings provide a novel understanding of hypertextuality as the process through which an organization is temporarily co-constituted by both inter- and intraorganizational discursive-material interactions across spaces and times.

Stigma's Effect on Social Interaction and Social Media Activity

Stigmatized topics, such as HIV/STD, likely constrain related information sharing in ways that should be apparent in social interactions both on and off the Internet. Specifically, the authors predicted that the more people perceive an issue as stigmatized, the less likely they are to talk about the issue both privately (with sexual partners and peers) and publicly (on Twitter). Study 1 tested the effect of stigma on conversations at the individual level: The authors asked a group of participants (N = 138) about perceived STD-testing stigma, interactions with a sexual partner, and conversations with peers about STD testing. Study 2 assessed whether health conditions, in the aggregate, were less likely to generate social media activity as a function of current stigmatization. Using 259,758 archived Twitter posts mentioning 13 medical conditions, the authors tested whether level of stigma predicted the volume of relevant social media conversation, controlling for each condition's amount of advocacy and Google search popularity from a user's perspective. Findings supported our hypotheses. Individuals who reported perceiving a given health conditions in more stigmatic ways also reported interacting less with others about that topic; Twitter results showed a similar pattern. Results also suggest a more complex story of influence, as funding from the National Institutes of Health (i.e., each conditions amount of advocacy) associated with the examined health conditions also predicted Twitter activity. Overall, these results indicated that stigma had a similar, dampening effect on face-to-face and Twitter interactions. Findings hold theoretical and practical implications, which are discussed.

The bottom-up formation and maintenance of a Twitter community

Purpose – The purpose of this paper is to explore the formation, maintenance and disintegration of a fringe Twitter community in order to understand if offline community structure applies to online communities. Design/methodology/approach – The research adopted Big Data methodological approaches in tracking user-generated contents over a series of months and mapped online Twitter interactions as a multimodal, longitudinal “social information landscape”. Centrality measures were employed to gauge the importance of particular user nodes within the complete network and time-series analysis were used to track ego centralities in order to see if this particular online communities were maintained by specific egos. Findings – The case study shows that communities with distinct boundaries and memberships can form and exist within Twitter’s limited user content and sequential policies, which unlike other social media services, do not support formal groups, demonstrating the resilience of desperate online users when their ideology overcome social media limitations. Analysis in this paper using social networks approaches also reveals that communities are formed and maintained from the bottom-up. Research limitations/implications – The research data is based on a particular data set which occurred within a specific time and space. However, due to the rapid, polarising group behaviour, growth, disintegration and decline of the online community, the data set presents a “laboratory” case from which many other online community can be compared with. It is highly possible that the case can be generalised to a broader range of communities and from which online community theories can be proved/disproved. Practical implications – The paper showed that particular group of egos with high activities, if removed, could entirely break the cohesiveness of the community. Conversely, strengthening such egos will reinforce the community strength. The questions mooted within the paper and the methodology outlined can potentially be applied in a variety of social science research areas. The contribution to the understanding of a complex social and political arena, as outlined in the paper, is a key example of such an application within an increasingly strategic research area – and this will surely be applied and developed further by the computer science and security community. Originality/value – The majority of researches that cover these domains have not focused on communities that are multimodal and longitudinal. This is mainly due to the challenges associated with the collection and analysis of continuous data sets that have high volume and velocity. Such data sets are therefore unexploited with regards to cyber-community research.

Theorizing Twitter Chat

Drawing on sociological, psychological and pedagogic theories, this article offers a conceptualisation of Twitter and Twitter chats as a continuous multilogue within communities of practice and/or communities of interest (CoP and CoI).This article examines the collapsing and increasingly overlapping boundaries of formal and informal education and locates Twitter chats within this overlapping area. Furthermore, conceptualising Twitter interactions as multilogue conversations, the article offers an object relational conception of knowledge, knowledge generation, and learning and a dynamic interpretation of Vygotsky’s Zone of Proximal Development (ZPD) and the notion of More Knowledgeable Other (MKO) to offer a theorisation and pedagogic perspective in relation to Twitter and Twitter chats.The article concludes by considering some of the implications of such a theorisation for individual and professional identity, learning and pedagogy.

User communities evolution in microblogs: A public awareness barometer for real world events

In social media, users' interactions are affected by real-world events which influence emergence and shifts of opinions and topics. Interactions around an event-related topic can be captured in a weighted network, while identification of connectivity and intensity patterns can improve understanding of users' interest on the topic. Community detection is studied here as a means to reveal groups of social media users with common interaction patterns in such networks. The proposed community detection approach identifies communities exploiting both structural properties and intensity patterns, while dynamics of communities' evolution around an event are revealed based on an iterative community detection and mapping scheme. We investigate the importance of considering interactions' intensity for community detection via a benchmarking process on synthetic graphs and propose a generic framework for: i) modeling user interactions, ii) identifying static and evolving communities around events, iii) extracting quantitative and qualitative measurements from the communities' timeline, iv) leveraging measurements to understand the events' impact. Two real-world case studies based on Twitter interactions demonstrate the framework's potential for capturing and interpreting associations among communities and events.

Discovering and validating influence in a dynamic online social network

Online human interactions take place within a dynamic hierarchy, where social influence is determined by qualities such as status, eloquence, trustworthiness, authority and persuasiveness. In this work, we consider topic-based twitter interaction networks, and address the task of identifying influential players. Our motivation is the strong desire of many commercial entities to increase their social media presence by engaging positively with pivotal bloggers and tweeters. After discussing some of the issues involved in extracting useful interaction data from a twitter feed, we define the concept of an active node subnetwork sequence. This provides a time-dependent, topic-based, summary of relevant twitter activity. For these types of transient interactions, it has been argued that the flow of information, and hence the influence of a node, is highly dependent on the timing of the links. Some nodes with relatively small bandwidth may turn out to be key players because of their prescience and their ability to instigate follow-on network activity. To simulate a commercial application, we build an active node subnetwork sequence based on key words in the area of travel and holidays. We then compare a range of network centrality measures, including a recently proposed version that accounts for the arrow of time, with respect to their ability to rank important nodes in this dynamic setting. The centrality rankings use only connectivity information (who tweeted whom, when), without requiring further information about the account type or message content, but if we post-process the results by examining account details, we find that the time-respecting, dynamic approach, which looks at the follow-on flow of information, is less likely to be ‘misled’ by accounts that appear to generate large numbers of automatic tweets with the aim of pushing out web links. We then benchmark these algorithmically derived rankings against independent feedback from five social media experts, given access to the full tweet content, who judge twitter accounts as part of their professional duties. We find that the dynamic centrality measures add value to the expert view, and can be hard to distinguish from an expert in terms of who they place in the top ten. These algorithms, which involve sparse matrix linear system solves with sparsity driven by the underlying network structure, can be applied to very large-scale networks. We also test an extension of the dynamic centrality measure that allows us to monitor the change in ranking, as a function of time, of the twitter accounts that were eventually deemed influential.

Effects of gender and tie strength on Twitter interactions

We examine the connection between language, gender, and social relationships, as manifested through communication patterns in social media. Building on an analysis of 78,000 Twitter messages exchanged between 1,753 gender-coded couples, we quantitatively study how the gender composition of conversing users influences the linguistic style apparent in the messages. Using Twitter data, we also model and control for the strength of ties between conversing users. Our findings show that, in line with existing theories, women use more intensifier adverbs, pronouns, and emoticons, especially when communicating with other women. Our results extend the understanding of gender-driven language use in the semi-public settings of social media services, and suggest implications for theory and insights for sociolinguistics.