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17
When social bots attack: Modeling susceptibility of users in online social networks
"... With the increasing importance of online social networks such as Twitter or Facebook, a new breed of computer programs, so-called social bots, emerged. Social bots are automatic or semi-automatic computer programs that mimic humans and/or human behavior in online social networks. Social bots can att ..."
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With the increasing importance of online social networks such as Twitter or Facebook, a new breed of computer programs, so-called social bots, emerged. Social bots are automatic or semi-automatic computer programs that mimic humans and/or human behavior in online social networks. Social bots can attack users in online social networks to pursue a variety of latent goals, such as to spread information or to influence targets. Without a deep understanding of the nature of such attacks or the susceptibility of users, the potential of social media as an instrument for facilitating discourse or democratic processes is in jeopardy. In this paper, we study data from the Social Bot Challenge 2011- an experiment conducted by the WebEcologyProject during 2011- in which three teams implemented a number of social bots that aimed to influence user behavior on Twitter. Using this data, we aim to develop models to (i) identify susceptible users among a set of targets and (ii) predict users’ level of susceptibility. We explore the predictiveness of three different groups of features (network, behavioral and linguistic features) for these tasks. Our results suggest that susceptible users tend to use Twitter for a conversational purpose and tend to be more open and social since they communicate with many different users, use more social words and show more affection than non-susceptible users.
Predicting responses to microblog posts
- In HLT-NAACL
, 2012
"... Microblogging networks serve as vehicles for reaching and influencing users. Predicting whether a message will elicit a user response opens the possibility of maximizing the virality, reach and effectiveness of messages and ad campaigns on these networks. We propose a discriminative model for predic ..."
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Cited by 8 (0 self)
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Microblogging networks serve as vehicles for reaching and influencing users. Predicting whether a message will elicit a user response opens the possibility of maximizing the virality, reach and effectiveness of messages and ad campaigns on these networks. We propose a discriminative model for predicting the likelihood of a response or a retweet on the Twitter network. The approach uses features derived from various sources, such as the language used in the tweet, the user’s social network and history. The feature design process leverages aggregate statistics over the entire social network to balance sparsity and informativeness. We use real-world tweets to train models and empirically show that they are capable of generating accurate predictions for a large number of tweets. 1
Anticipating discussion activity on community forums
- In: Third IEEE International Conference on Social Computing (SocialCom2011
, 2011
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Ignorance isn’t bliss: an empirical analysis of attention patterns in online communities
- In AES Conference on Social Computing
, 2012
"... Abstract—Online community managers work towards building and managing communities around a given brand or topic. A risk imposed on such managers is that their community may die out and its utility diminish to users. Understanding what drives attention to content and the dynamics of discussions in a ..."
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Abstract—Online community managers work towards building and managing communities around a given brand or topic. A risk imposed on such managers is that their community may die out and its utility diminish to users. Understanding what drives attention to content and the dynamics of discussions in a given community informs the community manager and/or host with the factors that are associated with attention, allowing them to detect a reduction in such factors. In this paper we gain insights into the idiosyncrasies that individual community forums exhibit in their attention patterns and how the factors that impact activity differ. We glean such insights through a two-stage approach that functions by (i) differentiating between seed posts- i.e. posts that solicit a reply- and non-seed posts- i.e. posts that did not get any replies, and (ii) predicting the level of attention that seed posts will generate. We explore the effectiveness of a range of features for predicting discussions and analyse their potential impact on discussion initiation and progress. Our findings show that the discussion behaviour of different communities exhibit interesting differences in terms of how attention is generated. Our results show amongst others that the purpose of a community as well as the specificity of the topic of a community impact which factors drive the reply behaviour of a community. For example, communities around very specific topics require posts to fit to the topical focus of the community in order to attract attention while communities around more general topics do not have this requirement. We also found that the factors which impact the start of discussions in communities often differ from the factors which impact the length of discussions. Index Terms—attention, online communities, discussion, popularity, user generated content I.
oro.open.ac.uk Automatic Identification of Best Answers in Online Enquiry Communities
"... and other research outputs Automatic identification of best answers in online enquiry communities Conference Item How to cite: Burel, Gregoire; He, Yulan and Alani, Harith (2012). Automatic identification of best answers in online ..."
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and other research outputs Automatic identification of best answers in online enquiry communities Conference Item How to cite: Burel, Gregoire; He, Yulan and Alani, Harith (2012). Automatic identification of best answers in online
Mining and comparing engagement dynamics across multiple social media platforms
- In In Proc. of ACM 2014 Web Science Conference
, 2014
"... and other research outputs Mining and comparing engagement dynamics across multiple social media platforms ..."
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and other research outputs Mining and comparing engagement dynamics across multiple social media platforms
oro.open.ac.uk Community Analysis through Semantic Rules and Role Composition Derivation
"... and other research outputs Community analysis through semantic rules and role composition derivation ..."
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and other research outputs Community analysis through semantic rules and role composition derivation
Semi-Automatic Semantic Moderation of Web Annotations
"... Many social media portals are featuring annotation functionality in order to integrate the end users ’ knowledge with existing digital curation processes. This facilitates extending existing metadata about digital resources. However, due to various levels of annotators ’ expertise, the quality of an ..."
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Many social media portals are featuring annotation functionality in order to integrate the end users ’ knowledge with existing digital curation processes. This facilitates extending existing metadata about digital resources. However, due to various levels of annotators ’ expertise, the quality of annotations can vary from excellent to vague. The evaluation and moderation of annotations (be they troll, vague, or helpful) have not been sufficiently analyzed automatically. Available approaches mostly attempt to solve the problem by using distributed moderation systems, which are influenced by factors affecting accuracy (such as imbalance voting). Despite this, we hypothesize that analyzing and exploiting both content and context dimensions of annotations may assist the automatic moderation process. In this research, we focus on leveraging the context and content features of social web annotations for semi-automatic semantic moderation. This paper describes the vision of our research, proposes an approach for semi-automatic semantic moderation, introduces an ongoing effort from which we collect data that can serve as a basis for evaluating our assumption, and report on lessons learned so far.
Determining Citizens ’ Opinions About Stories in the News Media
"... and other research outputs Determining citizens ’ opinions about stories in the news ..."
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and other research outputs Determining citizens ’ opinions about stories in the news
