Systems like Klout and Twitalyzer were developed as an attempt to measure the influence of users within social networks. Although the algorithms used by these systems are not public known, they have been widely used to rank users according to their influence, especially in the Twitter social network. As media companies might base their viral marketing campaigns on influence scores, users might attempt to boost their influence scores with simple mechanisms like following unknown users to be followed back or even interacting with those who reciprocate these actions. In this paper, we investigate if widely used influence scores are vulnerable and easy to manipulate. Our approach consists of developing Twitter bot accounts able to interact with real users to verify strategies that can increase their influence scores according to different systems. Our results show that it is possible to become influential using very simple strategies, suggesting that these systems should review their influence score algorithms to avoid accounting with automatic activity.
A particular challenge in the area of social media analysis is how to nd 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 speci c event or news story. Twitter provides the ability to curate users into lists, corresponding to meaningful topics or themes. Here we describe an approach for crowdsourcing the list building e orts of many di erent Twitter users, in order to identify topical communities. This approach involves the use of ensemble community nding to produce stable groupings of user lists, and by extension, individual Twitter users. We examine this approach in the context of a case study surrounding the detection of communities on Twitter relating to the London 2012 Olympics."
A study made by Theresa B. Clarke and C. Leigh Nelson
Purpose of the Study.
This study explores various outcomes associated with the incorporation of Twitter in the marketing classroom. To determine if Twitter use is a beneficial pursuit for marketing educators, we investigated classroom community, pedagogical effectiveness, and learning outcomes based on Twitter use and non-use within a required marketing course.
Method/Design and Sample.
For comparative purposes, a quasi-experimental design was employed across two semesters of the same undergraduate integrated marketing communications course. One semester (48 students) employed heavy Twitter use by both students and the instructor; the other semester had no Twitter use (36 students).
Independent sample t-tests (p < .05) were conducted to test the hypotheses. The course using Twitter had a significantly higher sense of classroom community and perception of pedagogical effectiveness. While there was no difference in perceived learning across the two groups, the group using Twitter outperformed the non-Twitter group on actual learning.
Value to Marketing Educators.
This study extends the small, but growing, body of knowledge on the use of Twitter in the higher education marketing classroom. Findings and recommendations add value to marketing educators by helping them make more informed decisions regarding whether or not to use Twitter in their courses.
Twitter introduced user lists in late 2009, allowing users to be grouped according to meaningful topics or themes. Lists have since been adopted by media outlets as a means of organising content around news stories. Thus the curation of these lists is important - they should contain the key information gatekeepers and present a balanced perspective on a story. Here we address this list curation process from a recommender systems perspective. We propose a variety of criteria for generating user list recommendations, based on content analysis, network analysis, and the "crowdsourcing" of existing user lists. We demonstrate that these types of criteria are often only successful for datasets with certain characteristics. To resolve this issue, we propose the aggregation of these dif ferent "views" of a news story on Twitter to produce more accurate user recommendations to support the curation process.
The hashtag (#) has become one of the most valuable assets in any modern marketing campaign. The brands that create the most effective ones and employ them well reap the benefits on Twitter. Those ......
This paper presents a study on Twitter use by SJ, the national Swedish train operator. The aim of the study is to investigate how SJ (known on Twitter under the handle @SJ_AB) made use of the platform at hand to communicate with customers during the tumultuous Christmas season of 2010. The paper features an analysis of an extensive data set containing 3,394 tweets tagged as relevant and archived during the winter of 2010/11. Findings show that while SJ are indeed utilizing Twitter to communicate with their customers, the discerned communicative patterns are mostly pertaining to what is described as an “office hour”–approach — making use of the Twitter platform in a way that largely conforms to established routines of organizational communication.
Anticipating repliers in online conversations is a fundamental challenge for computer mediated communication systems which aim to make textual, audio and/or video communication as natural as face to face communication. The massive amounts of data that social media generates has facilitated the study of online conversations on a scale unimaginable a few years ago. In this work we use data from Twitter to explore the predictability of repliers, and investigate the factors which inﬂuence who will reply to a message. Our results suggest that social factors, which describe the strength of relations between users, are more useful than topical factors. This indicates that Twitter users’ reply behavior is more impacted by social relations than by topics. Finally, we show that a binary classiﬁcation model, which differentiates between users who will and users who will not reply to a certain message, may achieve an F1-score of 0:74 when using social features.
Twitter has today added the ability to share tweets by email directly from the Twitter.com website. Clicking on the ‘More’ icon next to the normal Twitter controls will give you access to a pop-up email form.
Traditional media outlets are known to report political news in a biased way, and it matters because it could affect the political beliefs of the audience and can ultimately alter voting behavior. Hence tracking bias in everyday news and building a platform where people can receive balanced news information is important. In this work, we proposed a model that maps the news media outlets along an one dimensional dichotomous political spectrum using the co-subscriptions relationships inferred by Twitter links. By analyzing 7 million Twitter links, we have shown that the political dichotomy naturally arises on Twitter when we only consider direct media subscription. Furthermore, we demonstrated a real-time Twitter-based application that visualizes an ideological map of various media sources.