According to Kardara et al. (2012) there is “no standard method in the literature for evaluating the outcomes of an influence criterion”, but recent studies revealed that Twitter influencers may be “the users who produce original content that is frequently retweeted”, though “they avoid getting into discussions or reproducing others’ opinions” (online).
Romero et al. (2011) investigated to what extent individuals, governments and companies got the attention of popular users and influencers to spread their “ideas, policies, products and commentary” (p.113) on Twitter. The influence as such comes from the way certain users retweet and therefore re-distribute content that draws the attention of their followers.
Romero et al. employed an algorithm to measure the influence of Twitter. The algorithm is based on a corpus containing 22 million tweets with URLs, posted on Twitter in the 300 hours following 10 September 2009. Each URL got a timestamp and the processed data led to the following conclusions:
- it is rather hard to get the attention of Twitter users to “rise to the most trending” topics (p.114) given the large amount and frequency of the information distributed on the platform;
- popularity and influence do not necessarily contribute to information dissemination on Twitter. Their “correlation is weaker than it might be expected” (p.114);
- the information distribution on Twitter could be propagated better if content authors and their followers “actively engage rather than passively read it and cease to act on it” (p.114).
According to Romero et al. the influence is determined by four factors:
1) content novelty
2) resonance of the content published by the followed users and the content of the followers
3) content quality
4) frequency of the content users create (p.113).
A major stumbling block to the content propagation on the network is the followers’ passivity, even if influencers have a significant number of followers. Romero et al. state that this stumbling block is “often hard to overcome” (p.113).
Romero et al. attempt to introduce a new definition of “influence on social media”, which is not based on individual statistics – number of followers and retweets (RTs) – but on the structural properties of the Twitter platform along with the users’ behaviour and their passivity. The authors explain that the influence “depends on not only the size of the influenced audience, but also on their passivity” (p.113).
Furthermore the authors state that “high popularity does not necessarily imply high influence and vice-versa” (p.113). Twitter users somewhat compete for attention on the platform while distributing a significant information amount, which increases from one day to the next. Some users manage to get the attention of the others and this may lead to increased popularity.
Bakshy et al. (2011) studied the influence of 1.6 million Twitter users and their 74 million events that went out on the platform in two months in 2011. They discovered “the largest cascades tend to be generated by users who have been influential in the past and who have a large number of followers” (p.1). The authors attempt to define the term Twitter “influencers” as users “who exhibit some combination of desirable attributes – whether personal attributes like credibility, expertise, or enthusiasm, or network attributes such as connectivity or centrality – allows them to influence a disproportionately large number of others, possibly indirectly via a cascade of influence” (p.1). Bakshy et al. consider that both ordinary people and experts (journalists and other public figures) could be influencers on Twitter, depending on the configuration of their networks of followers and the role of their tweet content.
Pfitzner et al. (2012) found out that “highly emotional diverse tweets can have up to almost five times higher chances of being retweeted” (p.546). Following a study where sentiment extraction techniques were employed, they claim that Twitter in practice mainly involves two major actions: “information creation and subsequent distribution (tweeting) and pure information distribution (retweeting), with pronounced preference to the first” (p.543). The tweets carrying a “high emotional diversity have a better chance of being retweeted, hence influencing the distribution of information” (p.543).
 Kardara, M. et al. (2012), Influence Patterns in Topic Communities of Social Media, in WIMS ’12 Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
 Romero, M. D. et al., (2011), “Influence and passivity in social media” in Proceeding: WWW ’11 Proceedings of the 20th international conference companion on World Wide Web, pp. 113-114, ACM New York
 Bakshy, E. et al. (2011), Everyone’s an influencer: Quantifying influence on Twitter, in Proceedings of the fourth ACM international conference on Web search and data mining (2011), pp. 65-74
 Pfitzner R. et al. (2012), Emotional Divergence Influences Information Spreading in Twitter, in ICWSM, The AAAI