Conference Paper

Detecting emerging topics and trends via predictive analysis of ‘meme’ dynamics

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Abstract

Discovering and characterizing emerging topics and trends through analysis of Web data is of great interest to security analysts and policy makers. This paper considers the problem of monitoring social media to spot emerging memes - distinctive phrases which act as “tracers” for discrete cultural units - as a means of rapidly detecting new topics and trends. We have recently developed a method for predicting which memes will propagate widely and which will not, thereby enabling the discovery of significant topics. Here we demonstrate the efficacy of this approach through case studies involving political memes and memes associated with an emerging cyber threat.

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... Very recently a handful of investigations have shown the value of considering even simple and indirect measures of social influence, such as early social media " buzz " , when forming predictions. This work has produced useful prediction algorithms for an array of social phenomena, including markets [16][17][18][19][20][21], political and social movements [17,22], mobilization and protest behavior [23,24], epidemics [17,25], social media dynamics [26,27], and the evolution of cyber threats [28]. Recognizing the importance of accounting for social influence, this paper proposes a predictive methodology which explicitly considers the way individuals influence one another through their social networks. ...
... We suppose that the triggering event or emerging situation is given. Note that this is often the case in national security settings, and that additionally there exist techniques for discovering such events or issues in an automated or semi-automated manner e.g., [24,27]. It is assumed that data are available which provide a view of the early reaction of a relevant population to the trigger or issue of interest. ...
... However, we have demonstrated that it is possible to identify early indicators of movie success, such as temporal patterns in pre-release " buzz " , and to use these indicators to accurately predict ultimate box office revenues [39]. Recent research indicates that this result holds more generally, so that it may be more scientifically-sensible in many domains to pursue early warning rather than ex ante prediction goals [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. ...
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There is considerable interest in developing predictive capabilities for social diffusion processes, for instance to permit early identification of emerging contentious situations, rapid detection of disease outbreaks, or accurate forecasting of the ultimate reach of potentially viral ideas or behaviors. This paper proposes a new approach to this predictive analytics problem, in which analysis of meso-scale network dynamics is leveraged to generate useful predictions for complex social phenomena. We begin by deriving a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes taking place over social networks with realistic topologies; this modeling approach is inspired by recent work in biology demonstrating that S-HDS offer a useful mathematical formalism with which to represent complex, multi-scale biological network dynamics. We then perform formal stochastic reachability analysis with this S-HDS model and conclude that the outcomes of social diffusion processes may depend crucially upon the way the early dynamics of the process interacts with the underlying network's community structure and core-periphery structure. This theoretical finding provides the foundations for developing a machine learning algorithm that enables accurate early warning analysis for social diffusion events. The utility of the warning algorithm, and the power of network-based predictive metrics, are demonstrated through an empirical investigation of the propagation of political memes over social media networks. Additionally, we illustrate the potential of the approach for security informatics applications through case studies involving early warning analysis of large-scale protests events and politically-motivated cyber attacks.
... Of course, these security-relevant data are buried in the massive output of millions of online content generators, so that discovering and interpreting this information rapidly enough to be useful is very difficult. Recognizing both the opportunities and challenges associated with analyzing Web data for security applications, several researchers have studied this problem in recent years [1][2][3][4][5][6][7][8][9][10][11]. One interesting theme is the assessment and characterization of the ways in which the Web is used by jihadists, criminals, extremists , and others of interest to security professionals [e.g. ...
... Moreover, we require that a putative attribute of influential users have predictive power, so that users which possess this attribute are significantly more likely to produce viral content than are typical users. Recently it has been shown that predictive indicators of influence for bloggers include past influence and blog-graph position [6,35], which implies that determining if a given blogger is influential requires data on blog content, blog graph topology, and the temporal order of posts. ...
... More precisely, it is demonstrated in [21] that the social influences at work in collective dynamics processes act to reduce the predictive power of the " intrinsic " attributes of the process (e.g., option quality in social choice situations) and to increase the extent to which outcomes can be predicted based upon very early process dynamics [17,[20][21][22]. For example, it is shown in [21,17,6] that in social diffusion (e.g., of information, behavior, or disease), predictability depends crucially upon the topology of the underlying social network, and in particular on this network's community [24] and core-periphery [37] structures (see Figure 3and [21,17,6]). We have applied these findings to a broad range of prediction problems. ...
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... These data may, for instance, allow early detection of emerging issues and trends in populations of interest, which could be of considerable value [e.g. 6]. For example, contentious situations are much easier to resolve if discovered in their early stages, while nascent positive sentiments and activities can often be leveraged and amplified. ...
... Recognizing both the opportunities and challenges associated with analyzing Web data for security applications, several researchers have studied this problem in recent years [1][2][3][4][5][6][7][8][9][10][11]. One interesting theme is the assessment and characterization of the ways in which the Web is used by jihadists, criminals, extremists, and others of interest to security professionals [e.g. ...
... As a simple example, analyzing time series of documents, for instance blog posts or discussion threads, can reveal trends with important security implications [e.g. 6,17,20,31,33]. Interestingly, we have found that combining topological and temporal data associated with email communication makes it possible to accurately identify collaborating groups of individuals [31]. The underlying observation is simple: a strong indicator that an exchange of communications reflects a genuine collaboration, rather than say a more formal organization-sanctioned affiliation, is a tendency for collaborators to communicate in "bursts" of messages followed by periods of quiet. ...
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An enormous volume of security-relevant information is present on the Web, for instance in the content produced each day by millions of bloggers worldwide, but discovering and making sense of these data is very challenging. This paper considers the problem of exploring and analyzing the Web to realize three fundamental objectives: 1.) security-relevant information is covery, 2.) target situational awareness, typically by making (near) real-time inferences concerning events and activities from available observations, and 3.) predictive analysis, to include providing early warning for crises and forming predictions regarding likely outcomes of emerging issues and contemplated interventions. The proposed approach involves collecting and integrating three types of Web data -- textual, relational, and temporal - to perform assessments and generate insights that would be difficult or impossible to obtain using standard methods. We demonstrate the efficacy of the framework by summarizing a number of successful real-world deployments of the methodology.
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Question answering via Bayesian inference on lexical relationsModularity and community structure in networks
  • G Ramakrishnan
  • A Jadhav
  • A Joshi
  • S Chakrabarti
  • P Bhattacharyy�
Ramakrishnan, G., A. Jadhav, A. Joshi, S. Chakrabarti, and P. Bhattacharyy� "Question answering via Bayesian inference on lexical relations", Proc. Annual Meeting of the Association for Computational Linguistics, Sapporo, Japan, July 2003.Newman, M., "Modularity and community structure in networks", Proc. National Academy of Sciences USA, Vol. 103, pp. 8577-8582, 2006.
Countering Internet Radicalization in Southeast Asia
  • A Bergin
  • S Osman
  • C Ungerer
  • N Yasin
Modularity and community structure in networks
  • M Newman
Newman, M., "Modularity and community structure in networks", Proc. National Academy of Sciences USA, Vol. 103, pp. 8577-8582, 2006.
Social media analytics: Channeling the power of the blogosphere for marketing insight
  • P Melville
  • V Sindhwani
  • R Lawrence
Melville, P., V. Sindhwani, and R. Lawrence, "Social media analytics: Channeling the power of the blogosphere for marketing insight", Proc. Workshop on Information in Networks, New York, September 2009.
Prediction of social dynamics via Web analytics
  • R Colbaugh
  • K Glass
Colbaugh, R. and K. Glass, "Prediction of social dynamics via Web analytics", Sandia National Laboratories SAND Report, December 2010.
Question answering via Bayesian inference on lexical relations
  • G Ramakrishnan
  • A Jadhav
  • A Joshi
  • S Chakrabarti
  • P Bhattacharyya
Ramakrishnan, G., A. Jadhav, A. Joshi, S. Chakrabarti, and P. Bhattacharyya, "Question answering via Bayesian inference on lexical relations", Proc. Annual Meeting of the Association for Computational Linguistics, Sapporo, Japan, July 2003.