Birds of a Feather Tweet Together: Computational Techniques to Understand User Communities in Social Networks (original) (raw)

A Topological Approach for Detecting Twitter Communities with Common Interests

Ubiquitous Social Media Analysis, 2013

The efficient identification of communities with common interests is a key consideration in applying targeted advertising and viral marketing to online social networking sites. Existing methods involve large scale community detection on the entire social network before determining the interests of individuals within these communities. This approach is both computationally intensive and may result in communities without a common interest. We propose an efficient topological-based approach for detecting communities that share common interests on Twitter. Our approach involves first identifying celebrities that are representative of an interest category before detecting communities based on linkages among followers of these celebrities. We also study the network characteristics and tweeting behaviour of these communities, and the effects of deepening or specialization of interest on their community structures. In particular, our evaluation on Twitter shows that these detected communities comprise members who are well-connected, cohesive and tweet about their common interest.

Identifying Topical Twitter Communities via User List Aggregation

2012

Abstract: A particular challenge in the area of social media analysis is how to find communities within a larger network of social interactions. Here a community may be a group of microblogging users who post content on a coherent topic, or who are associated with a specific event or news story. Twitter provides the ability to curate users into lists, corresponding to meaningful topics or themes.

Topical cohesion of communities on Twitter

Procedia Computer Science, 2017

Nowadays, Online Social Networks (OSN) are commonly used by groups of users to communicate. Members of a family, colleagues, fans of a brand, political groups... There is an increasing demand for a precise identification of these groups, coming from brand monitoring, business intelligence and e-reputation management. However, a gap can be observed between the communities detected by many data analytics algorithms on OSN, and effective groups existing in real life: the detected communities often lack of meaning and internal semantic cohesion. Most of existing literature on OSN either focuses on the community detection problem in graphs without considering the topic of the messages exchanged, or concentrates exclusively on the messages without taking into account the social links. In this article, we support the hypothesis that communities extracted on OSN should be topically coherent. We therefore propose a model to represent the groups of interaction on Twitter, the reference on micro-blogging OSN, and two metrics to evaluate the topical cohesion of the detected communities. As an evaluation, we measure the topical cohesion of the groups of users detected by a baseline community detection algorithm.

Joint inference of user community and interest patterns in social interaction networks

Social Network Analysis and Mining

Online social media have become an integral part of our social beings. Analyzing conversations in social media platforms can lead to complex probabilistic models to understand social interaction networks. In this paper, we present a modeling approach for characterizing social interaction networks by jointly inferring user communities and interests based on social media interactions. We present several pattern inference models: i) Interest pattern model (IPM) captures population level interaction topics, ii) User interest pattern model (UIPM) captures user specific interaction topics, and iii) Community interest pattern model (CIPM) captures both community structures and user interests. We test our methods on Twitter data collected from Purdue University community. From our model results, we observe the interaction topics and communities related to two big events within Purdue University community, namely Purdue Day of Giving and Senator Bernie Sanders' visit to Purdue University as part of Indiana Primary Election 2016. Constructing social interaction networks based on user interactions accounts for the similarity of users' interactions on various topics of interest and indicates their community belonging further beyond connectivity. We observed that the degree-distributions of such networks follow power-law that is indicative of the existence of fewer nodes in the network with higher levels of interactions, and many other nodes with less interactions. We also discuss the application of such networks as a useful tool to effectively disseminate specific information to the target audience towards planning any largescale events and demonstrate how to single out specific nodes in a given community by running network algorithms.

Influence patterns in topic communities of social media

2012

Users of Social Media typically gather into communities on the basis of some common interest. Their interactions inside these on-line communities follow several, interesting patterns. For example, they differ in the level of influence they exert to the rest of the group: some community members are actively involved, affecting a large part of the community with their actions, while the majority comprises plain participants (e.g., information consumers). Identifying users of the former category lies on the focus of interest of many recent works, as they can be employed in a variety of applications, like targeted marketing.

Finding Twitter Communities with Common Interests using Following Links of Celebrities

Proceedings of the 3rd International Workshop on Modeling Social Media (MSM'12), in-conjunction with the 23rd ACM Conference on Hypertext and Social Media (HT'12), 2012

One important problem in target advertising and viral marketing on online social networking sites is the efficient identification of communities with common interests in large social networks. Existing methods involve large scale community detection on the entire social network before determining the interests of individuals within these communities. This approach is both computationally intensive and may result in communities without a common interest. We propose an efficient approach for detecting communities that share common interests on Twitter. Our approach involves first identifying celebrities that are representative of an interest category before detecting communities based on linkages among followers of these celebrities. We also study the characteristics of these communities and the effects of deepening or specialization of interest.

Finding interest groups from Twitter lists

ACM Symposium on Applied Computing, 2020

Twitter lists enable users of the social network to organize people they follow into groups of interest (e.g. politicians or journalists they like, favorite artists or athletes, authoritative figures in a given field, and so on). For the analyst, lists are a means of access to the structure of interactions between Twitter users and can be used to identify main actors of a field of interest. In this work, we introduce a methodology for constructing an edge-attributed multilayer network of Twitter users based on their membership to Twitter lists. We propose and validate a new approach that identifies local communities of users and their common interests from the constructed graph. We provide evidences that our method performs in a better way than global community detection approaches, and faster with as good results as competitive local methods.

Classifying Twitter Topic-Networks Using Social Network Analysis

Social Media + Society, 2017

As users interact via social media spaces, like Twitter, they form connections that emerge into complex social network structures. These connections are indicators of content sharing, and network structures reflect patterns of information flow. This article proposes a conceptual and practical model for the classification of topical Twitter networks, based on their network-level structures. As current literature focuses on the classification of users to key positions, this study utilizes the overall network structures in order to classify Twitter conversation based on their patterns of information flow. Four network-level metrics, which have established as indicators of information flow characteristics—density, modularity, centralization, and the fraction of isolated users—are utilized in a three-step classification model. This process led us to suggest six structures of information flow: divided, unified, fragmented, clustered, in and out hub-and-spoke networks. We demonstrate the v...

iSCUR: Interest and Sentiment-Based Community Detection for User Recommendation on Twitter

Lecture Notes in Computer Science, 2014

The increasing popularity of social networks has encouraged a large number of significant research works on community detection and user recommendation. The idea behind this work is that taking into account users' attitudes toward their own interests can bring benefits in performing such tasks. In this paper we describe (i) a novel method to infer sentiment-based communities without the requirement of obtaining the whole social structure, and (ii) a community-based approach to user recommendation. We take advantage of the SVO (sentiment-volume-objectivity) user profiling and the weighted Tanimoto similarity to evaluate user similarity for each topic. Afterwards we employ a clustering algorithm based on modularity optimization to find densely connected users and the Adamic-Adar tie strength to finally suggest the most relevant users. Preliminary experimental results on Twitter show the benefits of the proposed approach compared to some state-of-the-art user recommendation techniques.

Exploring Social Networks with Topical Analysis

As the number of social networking services (SNS) and their users grow, so does the complexity of individual networks as well as the amount of information to be consumed by the users. It is inevitable to reduce the complexity and information overload, and we have embarked exploring topical aspects of SNS to form refined topicbased semantic social networks. Our current work focuses on conversational aspects of SNS and attempt to utilize the notions of topic diversity and topic purity between two users sharing conversations. This topic-based analysis of SNS makes it possible to show different types of users and their conversational characteristics. It also shows the possibility of breaking down a huge "syntactic" social network into topic-based ones based on different interaction types, so that the resulting semantic social networks can be useful in designing various targeted services on online social networks.