Towards detection of influential sentences affecting reputation in Wikipedia.

Wikipedia has become the most frequently viewed online encyclopaedia website. Some sentences in Wikipedia articles have direct and obvious impact on people's opinions towards the mentioned named entities. This paper defines and tackles the problem of reputation-influential sentence detection in Wikipedia articles from various domains. We leverage multiple lexicons, to generate domain independent features. We generate topical features and word embedding features from unlabelled dataset, to boost the classification performance. We conduct several experiments, to prove the effectiveness of these features. We further adapt a two-step binary classification method, to perform multi-classification. Our evaluation results show that this method outperforms the state-of-the-art one-vs-one multi-classification method for this problem

Why Do Cascade Sizes Follow a Power-Law?

We introduce random directed acyclic graph and use it to model the information diffusion network. Subsequently, we analyze the cascade generation model (CGM) introduced by Leskovec et al. [19]. Until now only empirical studies of this model were done. In this paper, we present the first theoretical proof that the sizes of cascades generated by the CGM follow the power-law distribution, which is consistent with multiple empirical analysis of the large social networks. We compared the assumptions of our model with the Twitter social network and tested the goodness of approximation.Comment: 8 pages, 7 figures, accepted to WWW 201

Twitter as a first draft of the present - and the challenges of preserving it for the future

"This paper provides a framework for understanding Twitter as a historical source. We address digital humanities scholars to enable the transfer of concepts from traditional source criticism to new media formats, and to encourage the preservation of Twitter as a cultural artifact. Twitter has established itself as a key social media platform which plays an important role in public, real-time conversation. Twitter is also unique as its content is being archived by a public institution (the Library of Congress). In this paper we will show that we still have to assume that much of the contextual information beyond the pure tweet texts is already lost, and propose additional objectives for preservation." (author's abstract

What Do Computer Scientists Tweet? Analyzing the Link-Sharing Practice on Twitter

Twitter communication has permeated every sphere of society. To highlight and share small pieces of information with possibly vast audiences or small circles of the interested has some value in almost any aspect of social life. But what is the value exactly for a scientific field? We perform a comprehensive study of computer scientists using Twitter and their tweeting behavior concerning the sharing of web links. Discerning the domains, hosts and individual web pages being tweeted and the differences between computer scientists and a Twitter sample enables us to look in depth at the Twitter-based information sharing practices of a scientific community. Additionally, we aim at providing a deeper understanding of the role and impact of altmetrics in computer science and give a glance at the publications mentioned on Twitter that are most relevant for the computer science community. Our results show a link sharing culture that concentrates more heavily on public and professional quality information than the Twitter sample does. The results also show a broad variety in linked sources and especially in linked publications with some publications clearly related to community-specific interests of computer scientists, while others with a strong relation to attention mechanisms in social media. This refers to the observation that Twitter is a hybrid form of social media between an information service and a social network service. Overall the computer scientists’ style of usage seems to be more on the information-oriented side and to some degree also on professional usage. Therefore, altmetrics are of considerable use in analyzing computer science

Digital methods in a post-API environment

Qualitative and mixed methods digital social research often relies on gathering and storing social media data through the use of APIs (Application Programming Interfaces). In past years this has been relatively simple, with academic developers and researchers using APIs to access data and produce visualisations and analysis of social networks and issues. In recent years, API access has become increasingly restricted and regulated by corporations at the helm of social media networks. Facebook (the corporation) has restricted academic research access to Facebook (the social media platform) along with Instagram (a Facebook-owned social media platform). Instead, they have allowed access to sources where monetisation can easily occur, in particular, marketers and advertisers. This leaves academic researchers of digital social life in a difficult situation where API related research has been curtailed. In this paper we describe some rationales and methodologies for using APIs in social research. We then introduce some of the major events in academic API use that have led to the prohibitive situation researchers now find themselves in. Finally, we discuss the methodological and ethical issues this produces for researchers and, suggest some possible steps forward for API related research

It's getting crowded! : improving the effectiveness of microtask crowdsourcing

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In What Mood Are You Today?

The mood of individuals in the workplace has been well-studied due to its influence on task performance, and work engagement. However, the effect of mood has not been studied in detail in the context of microtask crowdsourcing. In this paper, we investigate the influence of one's mood, a fundamental psychosomatic dimension of a worker's behaviour, on their interaction with tasks, task performance and perceived engagement. To this end, we conducted two comprehensive studies; (i) a survey exploring the perception of crowd workers regarding the role of mood in shaping their work, and (ii) an experimental study to measure and analyze the actual impact of workers' moods in information findings microtasks. We found evidence of the impact of mood on a worker's perceived engagement through the feeling of reward or accomplishment, and we argue as to why the same impact is not perceived in the evaluation of task performance. Our findings have broad implications on the design and workflow of crowdsourcing systems