Feature analysis of spammers in social networks with active honeypots:a case study of Chinese microblogging networks

In this poster we presented our preliminary work on the study of spammer detection and analysis with 50 active honeypot profiles implemented on Weibo.com and QQ.com microblogging networks. We picked out spammers from legitimate users by manually checking every captured user's microblogs content. We built a spammer dataset for each social network community using these spammer accounts and a legitimate user dataset as well. We analyzed several features of the two user classes and made a comparison on these features, which were found to be useful to distinguish spammers from legitimate users. The followings are several initial observations from our analysis on the features of spammers captured on Weibo.com and QQ.com. ¦The following/follower ratio of spammers is usually higher than legitimate users. They tend to follow a large amount of users in order to gain popularity but always have relatively few followers. ¦There exists a big gap between the average numbers of microblogs posted per day from these two classes. On Weibo.com, spammers post quite a lot microblogs every day, which is much more than legitimate users do; while on QQ.com spammers post far less microblogs than legitimate users. This is mainly due to the different strategies taken by spammers on these two platforms. ¦More spammers choose a cautious spam posting pattern. They mix spam microblogs with ordinary ones so that they can avoid the anti-spam mechanisms taken by the service providers. ¦Aggressive spammers are more likely to be detected so they tend to have a shorter life while cautious spammers can live much longer and have a deeper influence on the network. The latter kind of spammers may become the trend of social network spammer. © 2012 IEEE.

Cost-effective node monitoring for online hot event detection in sina weibo microblogging

We propose a cost-effective hot event detection system over Sina Weibo platform, currently the dominant microblogging service provider in China. The problem of finding a proper subset of microbloggers under resource constraints is formulated as a mixed-integer problem for which heuristic algorithms are developed to compute approximate solution. Preliminary results show that by tracking about 500 out of 1.6 million candidate microbloggers and processing 15,000 microposts daily, 62% of the hot events can be detected five hours on average earlier than they are published by Weibo.

Connecting social media to e-commerce:cold-start product recommendation using microblogging information

In recent years, the boundaries between e-commerce and social networking have become increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign on the websites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper, we propose a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce websites to users at social networking sites in 'cold-start' situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users' social networking features to another feature representation for product recommendation. In specific, we propose learning both users' and products' feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users' social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service Sina Weibo and the largest Chinese B2C e-commerce website JingDong have shown the effectiveness of our proposed framework.

Microblogging Temporal Summarization: Filtering Important Twitter Updates for Breaking News

While news stories are an important traditional medium to broadcast and consume news, microblogging has recently emerged as a place where people can dis- cuss, disseminate, collect or report information about news. However, the massive information in the microblogosphere makes it hard for readers to keep up with these real-time updates. This is especially a problem when it comes to breaking news, where people are more eager to know “what is happening”. Therefore, this dis- sertation is intended as an exploratory effort to investigate computational methods to augment human effort when monitoring the development of breaking news on a given topic from a microblog stream by extractively summarizing the updates in a timely manner. More specifically, given an interest in a topic, either entered as a query or presented as an initial news report, a microblog temporal summarization system is proposed to filter microblog posts from a stream with three primary concerns: topical relevance, novelty, and salience. Considering the relatively high arrival rate of microblog streams, a cascade framework consisting of three stages is proposed to progressively reduce quantity of posts. For each step in the cascade, this dissertation studies methods that improve over current baselines. In the relevance filtering stage, query and document expansion techniques are applied to mitigate sparsity and vocabulary mismatch issues. The use of word embedding as a basis for filtering is also explored, using unsupervised and supervised modeling to characterize lexical and semantic similarity. In the novelty filtering stage, several statistical ways of characterizing novelty are investigated and ensemble learning techniques are used to integrate results from these diverse techniques. These results are compared with a baseline clustering approach using both standard and delay-discounted measures. In the salience filtering stage, because of the real-time prediction requirement a method of learning verb phrase usage from past relevant news reports is used in conjunction with some standard measures for characterizing writing quality. Following a Cranfield-like evaluation paradigm, this dissertation includes a se- ries of experiments to evaluate the proposed methods for each step, and for the end- to-end system. New microblog novelty and salience judgments are created, building on existing relevance judgments from the TREC Microblog track. The results point to future research directions at the intersection of social media, computational jour- nalism, information retrieval, automatic summarization, and machine learning.

Applying social technology to Business Process Lifecycle Management

In recent years social technologies such as wikis, blogs or microblogging have seen an exponential growth in the uptake of their user base making this type of technology one of the most significant networking and knowledge sharing platforms for potentially hundreds of millions of users. However, the adoption of these technologies has been so far mostly for private purposes. First attempts have been made to embed features of social technologies in the corporate IT landscape, and Business Process Management is no exception. This paper aims to consolidate the opportunities for integrating social technologies into the different stages of the business process lifecycle. Thus, it contributes to a conceptualization of this fast growing domain, and can help to categorize academic and corporate development activities.

Towards semantic search and inference in electronic medical records : an approach using concept-based information retrieval

For more than a decade research in the field of context aware computing has aimed to find ways to exploit situational information that can be detected by mobile computing and sensor technologies. The goal is to provide people with new and improved applications, enhanced functionality and better use experience (Dey, 2001). Early applications focused on representing or computing on physical parameters, such as showing your location and the location of people or things around you. Such applications might show where the next bus is, which of your friends is in the vicinity and so on. With the advent of social networking software and microblogging sites such as Facebook and Twitter, recommender systems and so on context-aware computing is moving towards mining the social web in order to provide better representations and understanding of context, including social context. In this paper we begin by recapping different theoretical framings of context. We then discuss the problem of context- aware computing from a design perspective.

Twitter and society : an introduction

Since its launch in 2006, Twitter has turned from a niche service to a mass phenomenon. By the beginning of 2013, the platform claims to have more than 200 million active users, who “post over 400 million tweets per day” (Twitter, 2013). Its success is spreading globally; Twitter is now available in 33 different languages, and has significantly increased its support for languages that use non-Latin character sets. While Twitter, Inc. has occasionally changed the appearance of the service and added new features—often in reaction to users’ developing their own conventions, such as adding ‘#’ in front of important keywords to tag them—the basic idea behind the service has stayed the same: users may post short messages (tweets) of up to 140 characters and follow the updates posted by other users. This leads to the formation of complex follower networks with unidirectional as well as bidirectional connections between individuals, but also between media outlets, NGOs, and other organisations. While originally ‘microblogs’ were perceived as a new genre of online communication, of which Twitter was just one exemplar, the platform has become synonymous with microblogging in most countries. A notable exception is Sina Weibo, popular in China where Twitter is not available. Other similar platforms have been shut down (e.g., Jaiku), or are being used in slightly different ways (e.g., Tumblr), thus making Twitter a unique service within the social media landscape.

Detecting news topics from microblogs using sequential pattern mining

This thesis presents a sequential pattern based model (PMM) to detect news topics from a popular microblogging platform, Twitter. PMM captures key topics and measures their importance using pattern properties and Twitter characteristics. This study shows that PMM outperforms traditional term-based models, and can potentially be implemented as a decision support system. The research contributes to news detection and addresses the challenging issue of extracting information from short and noisy text.

Professional learning in 140 characters

The World Wide Web has grown into a global information and communication space with more than a billion users and has entered a new, more social and participatory phase where people create and manage online content rather than just viewing it; a place where people can communicate knowledge, share resources and participate in social networks. Online social networks are being used to support professional learning where groups of people are using the Web to communicate and collaborate in order to build and share knowledge and form professional learning networks (PLNs). This session will present the results of research into how microblogging, a form of online social networking, is being employed by educators to support their professional learning. The study examined activities and perceptions of a group of educators in order to provide an insight into how and why they engage in microblogging and the value they place on microblogging as a professional learning tool. The session will outline the advantages of microblogging as a professional learning tool; the range of behaviours and activities that are undertaken to support professional learning; and the implications for practice.

Remaking Guangzhou: Political engagement and place-making on Sina Weibo

This study uses the concept of ‘place-making’ to consider political engagement on Sina Weibo, one of the most popular microblogging services in China. Besides articulating statepublic confrontation during major social controversies, Weibo has been used to recollect and renarrate the memories of a city, such as Guangzhou, where dramatic social and cultural changes took place during the economic reform era. The Chinese government’s ongoing project to create a culturally indifferent ‘national identity’ triggers a defensive response from local places. Through consuming news and information about leisure and entertainment in Guangzhou, the digital narration of the city becomes an important source for Guangzhou people to learn about their geo-identity, and the kind of rights and responsibility attaching to it.

Enterprise-related crisis communication on Twitter

The rise of social media as communication channels has enabled customers to provide feedback or to ask for assistance quickly and easily. In the context of brand crises, the microblogging platform Twitter is highly relevant because of its ability to support information sharing. By investigating communication on Twitter, the authors examine Twitter activity patterns based on a dataset of some 240,000 tweets during two major brand crises affecting the Australian airline Qantas – the volcanic ash cloud caused by the eruption of Chilean volcano Puyehue in June 2011, and the global grounding of Qantas flights by management in the course of an industrial dispute in October/November 2011. Through this case study we find that characteristics of communication change significantly during different stages of the crisis. Further, we demonstrate that different kinds of crisis result in different communication patterns on Twitter.

Remaking Guangzhou: Geo-identity and place-making on Sina Weibo

This study uses the concept of 'place-making' to consider the formation of geo-identity on Sina Weibo, one of the most popular microblogging services in China. Besides articulating state-public confrontation during major social controversies, Weibo has been used to recollect and re-narrate the memories of a city, such as Guangzhou, where dramatic social and cultural changes took place during the economic reform era. This study aims to explore how Weibo sustains political engagement through maintaining Guangzhou people's sense of belonging to their city. By collecting data from a Weibo group over a period of twelve months, I argue that Weibo politics not only takes place during a contentious events, but is sustained within the realm of everyday life. This study has the potential to contribute to the limited knowledge of Weibo use during non-contentious period in China, hence broadening the notion of popular polity in the age of social media.

Reconfiguring Guangzhou identity – a study of place and locality on Sina Weibo

This thesis investigates the role of Chinese microblogging platform Sina Weibo in how the people of Guangzhou understand and negotiate their sense of locality. The geo-identity approach used in this thesis opens up a new approach to explore the complex power relationships that structure our society in and through digital media. It finds that although the Chinese government is trying to orchestrate a homogeneous sense of national belonging, Weibo is constantly reinforcing people's awareness of and identification with the local. The findings show that as new communication technologies and practices reconfigure people's daily experience and social lives, they redefine our sense of self and belonging.

Teacher professional conversations – the oz- Teachers story

The oz-Teachers listserv, an email list for teachers, ran continuously for 20 years, from 1995 to 2015. It provided the technical infrastructure for professional communication with the majority of its members being Australian teachers based in classrooms across the country. An analysis of the list archives provides us with interesting insights as to how teachers learn from and within communities of their peers and how such communities offer social and educational affordances to allow teachers to generate and enhance their own learning. This paper begins with a brief review of the response to the announcement of the list’s closure. It then moves to a report of the types of communication which emerged from the list over time with comparisons drawn from extant research, namely, an early analysis of email lists and a more contemporary study of teacher communication through microblogging. We identified 14 categories with eight of these being paired, namely, as asking/seeking and responding/giving. The key finding of this analysis was that the list, and its professional discussions, were sustained through reciprocity and collective intelligence, that is, sharing of information and resources and that this was evident through the life of the listserv.

Twitter as a medium for professional networking

This study aims to explore the use of Twitter for professional purposes. The researcher discovered that Twitter is widely perceived as an information ground in online spaces. Information grounds are social settings where information, people, and places come together to create an information flow within a physical environment. Twitter provides a sense of place as well as a sense of belonging that enables IT professionals to use Twitter for professional development. The data for this study were collected using online observations and interviews. The online observations helped the researcher to distinguish the ‘information behaviours’ (the objective and observable actions) of the participants. The interviews were used to understand the way IT professionals use Twitter for professional purposes through their own individual perspectives. The data were analysed using a constructive grounded theory. The findings show that building professional networking is extremely important to IT professionals; rather than the information-seeking and information-sharing aspects of Twitter. Building professional networking in microblogging has a significant influence on an individual’s professional development. The results also demonstrate that IT professionals are more likely to exploit their weak-ties rather than their strong-ties on Twitter. In short, these users experience Twitter as a real place or ‘information grounds’ where they meet and socialise with experts.