Information Diffusion on Twitter: Everyone Has Its Chance, But All Chances Are Not Equal

Cazabet, Rémy; Pervin, Nargis; Toriumi, Fujio; Takeda, Hideaki

doi:10.1109/sitis.2013.84

Cited by 19 publications

(22 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Influenced users (often referred to as activated users) are those who engaged with a post through liking, commenting, sharing, or retweeting. Studies have evaluated the diffusion of a tweet through its retweetability (ie, the length of the retweet chain) [55,58]. According to Wang et al [59], a message tends to propagate better if not only your friends, but also friends of friends, are getting involved in the conversation.…”

Section: Influencersmentioning

confidence: 99%

“…The factor that is most commonly applied to estimate the level of user's influence is the number of his or her followers. It has been demonstrated that there is a correlation between the number of followers and the length of the retweet chain [55]. The study by Yang and Counts [56] suggested that the number of times a user has been mentioned in the past is a good predicator of the number of his or her followers who might be influenced.…”

Section: Influencersmentioning

confidence: 99%

See 1 more Smart Citation

Tweet for Behavior Change: Using Social Media for the Dissemination of Public Health Messages

Gough¹,

Hunter²,

Ajao³

et al. 2017

JMIR Public Health Surveill

212

146

View full text Add to dashboard Cite

BackgroundSocial media public health campaigns have the advantage of tailored messaging at low cost and large reach, but little is known about what would determine their feasibility as tools for inducing attitude and behavior change.ObjectiveThe aim of this study was to test the feasibility of designing, implementing, and evaluating a social media–enabled intervention for skin cancer prevention.MethodsA quasi-experimental feasibility study used social media (Twitter) to disseminate different message “frames” related to care in the sun and cancer prevention. Phase 1 utilized the Northern Ireland cancer charity’s Twitter platform (May 1 to July 14, 2015). Following a 2-week “washout” period, Phase 2 commenced (August 1 to September 30, 2015) using a bespoke Twitter platform. Phase 2 also included a Thunderclap, whereby users allowed their social media accounts to automatically post a bespoke message on their behalf. Message frames were categorized into 5 broad categories: humor, shock or disgust, informative, personal stories, and opportunistic. Seed users with a notable following were contacted to be “influencers” in retweeting campaign content. A pre- and postintervention Web-based survey recorded skin cancer prevention knowledge and attitudes in Northern Ireland (population 1.8 million).ResultsThere were a total of 417,678 tweet impressions, 11,213 engagements, and 1211 retweets related to our campaign. Shocking messages generated the greatest impressions (shock, n=2369; informative, n=2258; humorous, n=1458; story, n=1680), whereas humorous messages generated greater engagement (humorous, n=148; shock, n=147; story, n=117; informative, n=100) and greater engagement rates compared with story tweets. Informative messages, resulted in the greatest number of shares (informative, n=17; humorous, n=10; shock, n=9; story, n=7). The study findings included improved knowledge of skin cancer severity in a pre- and postintervention Web-based survey, with greater awareness that skin cancer is the most common form of cancer (preintervention: 28.4% [95/335] vs postintervention: 39.3% [168/428] answered “True”) and that melanoma is most serious (49.1% [165/336] vs 55.5% [238/429]). The results also show improved attitudes toward ultraviolet (UV) exposure and skin cancer with a reduction in agreement that respondents “like to tan” (60.5% [202/334] vs 55.6% [238/428]).ConclusionsSocial media–disseminated public health messages reached more than 23% of the Northern Ireland population. A Web-based survey suggested that the campaign might have contributed to improved knowledge and attitudes toward skin cancer among the target population. Findings suggested that shocking and humorous messages generated greatest impressions and engagement, but information-based messages were likely to be shared most. The extent of behavioral change as a result of the campaign remains to be explored, however, the change of attitudes and knowledge is promising. Social media is an inexpensive, effective method for delivering public health messages. How...

show abstract

Section: Influencersmentioning

confidence: 99%

Section: Influencersmentioning

confidence: 99%

Tweet for Behavior Change: Using Social Media for the Dissemination of Public Health Messages

Gough¹,

Hunter²,

Ajao³

et al. 2017

JMIR Public Health Surveill

212

146

View full text Add to dashboard Cite

show abstract

“…Similarly, Remy et al studied the impact of the number of followers of users on the capacity to propagate their message. Interestingly, they showed that the impact of users with a lot of followers is not statistically greater than users with a few followers [5]. This features is also considered in our work to analyze its impact on the retweetability.…”

Section: Related Workmentioning

confidence: 93%

Predicting information diffusion on Twitter – Analysis of predictive features

Hoang

Mothe

2018

Journal of Computational Science

View full text Add to dashboard Cite

Information propagation on online social network focuses much attention in various domains as varied as politics, fact checking, or marketing. Modeling information diffusion in such growing communication media is crucial in order both to understand information propagation and to better control it. Our research aims at predicting whether a post is going to be forwarded or not. Moreover, we aim at predicting how much it is going to be diffused. Our model is based on three types of features: user-based, time-based and content-based. Using three collections corresponding to a total of about 16 millions of tweets, we show that our model improves of about 5% F-measure compared to the state of the art, both when predicting if a tweet is going to be re-tweeted and when predicting how popular it will be. F-measure in our model is between 70% and 82%, depending on the collection. We also show that some features we introduced are very important to predict retweetability such as the numbers of followers and number of communities that a user belongs to. Our contribution in this paper is twofold: firstly we defined new features to represent tweets in order to predict their possible propagation; secondly we evaluate the model we built on top of both features from the literature and features we defined on three collections and show the usefulness of our features in the prediction.

show abstract

“…Then, for each valid citation, we create a link from each of the authors of the citing article to each of the authors of the cited one, excluding selfcitations. -We use a Twitter dataset described in Toriumi et al (2013), Remy et al 2013), that contains between 80 and 90 % of all tweets published between Japanese users on a period of 22 days (around the Japanese earthquake and Tsunami of 2011). We kept only tweets between active users, defined as having tweeted at least Fig.…”

Section: Description Of Analyzed Networkmentioning

confidence: 99%

“…We have to stress that these being real datasets, the behaviors of users might change during the observed dataset. Both ENRON and Twitter datasets have been collected in the time of crisis, for instance, during which the behavior of users can change (Remy et al 2013). …”

Section: Description Of Analyzed Networkmentioning

confidence: 99%

Characterizing the nature of interactions for cooperative creation in online social networks

Cazabet

Takeda

Hamasaki

2015

Soc. Netw. Anal. Min.

Self Cite

View full text Add to dashboard Cite

Many aspects of online social networks (OSN) have been studied in recent years. In this article, we focus on the question of interactions in large OSN. We propose methods to study these interactions, and apply them on a platform called Nico Nico Douga (NND), with the aim of understanding cooperative behaviors, taking the form of collective creation of music videos in NND. Our first contribution is a method that, starting from the network of interactions between users, evaluates three aspects: the impact of the social structure on these interactions, their concentration, and their reciprocity. We characterize the nature of interactions in NND, and compare it with four different datasets. We find that interactions in NND are more similar to a diffusion process, such as retweets in Tweeter, than to interpersonal communications, or even to cooperation in science. Our second contribution is a typology of roles for productions in a cooperative process. These roles are attributed based on the neighborhood of the nodes in the network of references between productions. We define direct roles, relative roles, and indirect roles. We subsequently study the frequency of these roles in NND. We show a correlation between the category of the contribution of a video (song, animation, etc.) and its probability of having a certain role. We also find a positive correlation between the most active users and the production of videos playing an important role in the cooperation process.

show abstract

Information Diffusion on Twitter: Everyone Has Its Chance, But All Chances Are Not Equal

Cited by 19 publications

References 16 publications

Tweet for Behavior Change: Using Social Media for the Dissemination of Public Health Messages

Tweet for Behavior Change: Using Social Media for the Dissemination of Public Health Messages

Predicting information diffusion on Twitter – Analysis of predictive features

Characterizing the nature of interactions for cooperative creation in online social networks

Contact Info

Product

Resources

About