Campaign extraction from social media

Lee, Kyumin; Caverlee, James; Cheng, Zhiyuan; Sui, Daniel Z.

doi:10.1145/2542182.2542191

Cited by 34 publications

(27 citation statements)

References 47 publications

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“…Fortunately, most forms of this type of vote manipulation can be easily detected and stopped with spam detection and prevention techniques [21,33]. As part of a larger strategy, Reddit now encourages hyperlinks between subreddits to be tagged with a no-participation URL, which restricts access for non-subscribers of the subreddit to read-only, in order to prevent "cross-subreddit contamination 1 ."…”

Section: Vote-based Manipulationmentioning

confidence: 99%

Rating Effects on Social News Posts and Comments

Glenski

Weninger

2017

ACM Trans. Intell. Syst. Technol.

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At a time when information seekers first turn to digital sources for news and opinion, it is critical that we understand the role that social media plays in human behavior. This is especially true when information consumers also act as information producers and editors through their online activity. In order to better understand the effects that editorial ratings have on online human behavior, we report the results of a two large-scale in-vivo experiments in social media. We find that small, random rating manipulations on social media posts and comments created significant changes in downstream ratings resulting in significantly different final outcomes. We found positive herding effects for positive treatments on posts, increasing the final rating by 11.02% on average, but not for positive treatments on comments. Contrary to the results of related work, we found negative herding effects for negative treatments on posts and comments, decreasing the final ratings on average, of posts by 5.15% and of comments by 37.4%. Compared to the control group, the probability of reaching a high rating (≥2000) for posts is increased by 24.6% when posts receive the positive treatment and for comments is decreased by 46.6% when comments receive the negative treatment.

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Section: Vote-based Manipulationmentioning

confidence: 99%

Rating Effects on Social News Posts and Comments

Glenski

Weninger

2017

ACM Trans. Intell. Syst. Technol.

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“…However, they neglect to utilize valuable user-generated content in which users express their opinions. On the other hand, content-based methods only utilize user-generated content [22]. Nevertheless, the content on social media is extremely noisy, resulting in the failure in detecting communities effectively.…”

Section: Related Workmentioning

confidence: 99%

Sentiment-driven Community Profiling and Detection on Social Media

Salehi

Özer

Davulcu

2018

Proceedings of the 29th on Hypertext and Social Media

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Web 2.0 helps to expand the range and depth of conversation on many issues and facilitates the formation of online communities. Online communities draw various individuals together based on their common opinions on a core set of issues. Most existing community detection methods merely focus on discovering communities without providing any insight regarding the collective opinions of community members and the motives behind the formation of communities. Several efforts have been made to tackle this problem by presenting a set of keywords as a community profile. However, they neglect the positions of community members towards keywords, which play an important role for understanding communities in the highly polarized atmosphere of social media. To this end, we present a sentiment-driven community profiling and detection framework which aims to provide community profiles presenting positive and negative collective opinions of community members separately. With this regard, our framework initially extracts key expressions in users' messages as representative of issues and then identifies users' positive/negative attitudes towards these key expressions. Next, it uncovers a low-dimensional latent space in order to cluster users according to their opinions and social interactions (i.e., retweets). We demonstrate the effectiveness of our framework through quantitative and qualitative evaluations.

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“…D'Avanzo and Pilato on the other hand focused on the opinions given by the customers on social networks, and emphasized on how they gave direction to shopping activities [28]. Lee et al developed a framework that detected data on product campaigns in the shares [29]. Spina et al conducted a study that distinguished whether some words referring to specific brands and also used in daily life are in fact brands or ordinary words [30].…”

Section: Literature Reviewmentioning

confidence: 99%

Advertising recommendation system based on dynamic data analysis on Turkish speaking Twitter users

Sevli

Küçüksille

2017

Teh. vjesn.

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Preporučeni sustav oglašavanja zasnovan na dinamičkoj analizi podataka turskih korisnika Twittera Original scientific paper Online environments and especially social networks have become a great alternative to advertisement publishing. In order to accomplish effective advertising it is important that the contents coincide with the expectations of the target audience. Considering that expectations may change over time, it is required to identify the orientation of the users in real time and dynamically. In this study, the messages shared by Turkish Twitter users were analysed in real time and the instant expectations of the users have been identified. To perform this work, a web service was designed which analyses the user's profile and presents the advertisements that suit best to expectations. A method called Heuristic Pruning Method (HPM) has been revealed in order to filter the most appropriate advertising content. The developed system has been tested on a voluntary participant group who actively uses Twitter, and the effectiveness of the system is demonstrated by the received feedback. Keywords: advertising recommendation; content filtering; map reduce; social network marketing Preporučeni sustav oglašavanja zasnovan na dinamičkoj analizi podataka turskih korisnika TwitteraIzvorni znanstveni članak Online okruženja, a posebno društvene mreže postala su snažna alternative objavljivanju oglasa. Za učinkovito oglašavanje važno je da se sadržaj poistovjećuje s očekivanjima ciljane publike. Uzimajući u obzir da se očekivanja mogu s vremenom promijeniti, potrebno je u realnom vremenu i dinamički prepoznati orijentaciju korisnika. U ovom su se radu u realnom vremenu analizirale poruke turskih korisnika Twittera i identificirala njihova trenutna očekivanja. U tu je svrhu dizajnirana web usluga koja analizira profil korisnika i daje oglase koji najbolje odgovaraju očekivanjima. Za filtriranje odgovarajućeg sadržaja oglašavanja korištena je metoda nazvana heuristička metoda odstranjivanja suvišnog (Heuristic Pruning Method -HPM). Razvijeni sustav je testiran na grupi volontera, aktivnih korisnika Twittera, a učinkovitost sustava se pokazala dobivenom povratnom informacijomfeedbackom.

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Campaign extraction from social media

Cited by 34 publications

References 47 publications

Rating Effects on Social News Posts and Comments

Rating Effects on Social News Posts and Comments

Sentiment-driven Community Profiling and Detection on Social Media

Advertising recommendation system based on dynamic data analysis on Turkish speaking Twitter users

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