<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div><div><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div>Call for Papers</div><div><br></div><div><b>19th Americas Conference on Information Systems</b></div><div>Chicago, Illinois, August 15-17, 2013</div><div>Track: Intelligence and Intelligent Systems</div><div><br></div><div><b><font class="Apple-style-span" size="5">MINI-TRACK: Social Media Analytics – Methods and Tools</font></b></div><div>Mini-Track Chairs: Stefan Stieglitz, Axel Bruns</div><div><br></div><div><b>DESCRIPTION</b></div><div>In recent years social media platforms have attracted millions of users. This development challenges organizations (e.g. enterprises, NGOs, and political institutions) twofold: 1) Public social media such as Facebook, Twitter or blogs are increasingly used by stakeholders to communicate among each other or to get in contact with organizations. Based on this, organizations are able to learn more about stakeholders’ needs and opinions. On the other hand stakeholders may also publicly complain about products or persons and in some cases certain issues may evolve to crisis situations harming the organization. 2) Social media are also increasingly adapted to be used for internal collaboration among employees. By providing social business software, organizations could profit by improving their knowledge management or by identifying new ideas and innovations generated by employees. On the other hand, the growing amount of unstructured data also means that it becomes increasingly difficult to “manage” the community or to identify useful content.</div><div>In both contexts it becomes important to be able to gather and analyze the unstructured mass data which is created in social media. Therefore, appropriate methods and technologies have to be developed, combined, and applied. However, this is considered a challenging task due to the large number of different social media platforms and the vast amount as well as complexity of information and unstructured data. One reason for this is that information of this kind is not compiled by means of classic information retrieval as done by common search engines. Identifying distinct subjects, gathering, and analyzing information, and aggregating results is therefore still a challenge, which, however, is being tackled by “social media analytics” (Zeng et al. 2010; Agrawal et al. 2011; Leskovec 2011; Nagarajan et al. 2011). </div><div>Examples for methods of social media analytics are automatic content analysis, machine learning, sentiment analysis, manual content analysis, social network analysis, and genre analysis. According to Zeng et al. (2010) social media analytics is supposed to provide tools and frameworks to collect, monitor, analyze, summarize, and visualize social media data in an automated way due to the massive amount of (mostly unstructured) social media data. Currently there exists a wide spectrum of proprietary tools as well as open source software which help researchers to gather and analyze social media data. However, researchers still have to face several technical or methodological problems, e.g. changing APIs, intransparent software, and inaccurate automatic content analysis. At the same time there is a lack of frameworks describing systematic approaches and appropriate methods and techniques required for tracking, monitoring and analyzing content from social media in different contexts (Bruns & Stieglitz 2012; Stieglitz & Dang-Xuan 2012). </div><div>The goal of this mini-track is to provide a forum for academics and practitioners to identify and explore the methodological and technical issues of social media analytics. Empirical (both quantitative and qualitative) as well as theoretical work is welcome.</div><div><br></div><div><b>SUGGESTED TOPICS</b></div><div>In this workshop we are inviting papers from the following fields (however, it is not limited to these areas of research):</div><div><ul><li>Methods of Social Media Analytics</li><li>Theories related to Social Media Analytics</li><li>Exploration of patterns and metrics in social media communication</li><li>Technologies related to Social Media Analytics</li><li>Crisis detection / early warning approaches based on social media data</li><li>Predictive approaches based on social media communication</li><li>Dynamics in social media communication</li><li>Appearance of ad hoc events in social media</li><li>Application of social network analysis on social media data</li><li>Data storage of Social Media content</li></ul></div><div><br></div><div><b>SUBMISSION PROCEDURE</b></div><div>Submit your manuscript using the Bepress system at <a href="http://amcis2013.aisnet.org/">http://amcis2013.aisnet.org</a></div><div><br></div><div><b>IMPORTANT DATES</b></div><div><ul><li>January 4, 2013: Paper submissions officially begins</li><li>February 22, 2013: Paper Submission Deadline 11:59 PM CST</li><li>April 22, 2013: Program Chairs Notify Authors of Paper Acceptance</li><li>May 9, 2013: Camera-ready Copy of Accepted Papers Due</li><li>Updated information at <a href="http://amcis2013.aisnet.org/">http://amcis2013.aisnet.org</a></li></ul></div><div><br></div><div><b>MINI-TRACK CHAIRS</b></div><div><br></div><div><b>Stefan Stieglitz</b></div><div>University of Münster</div><div>Department of Information Systems</div><div>Münster, Germany</div><div><a href="mailto:stefan.stieglitz@uni-muenster.de">stefan.stieglitz@uni-muenster.de</a></div><div><br></div><div><b>Axel Bruns</b></div><div>ARC Centre of Excellence for Creative Industries and Innovation</div><div>Queensland University of Technology</div><div>Brisbane, Australia</div><div><a href="mailto:a.bruns@qut.edu.au">a.bruns@qut.edu.au</a></div><div><br></div><div><b>REFERENCES</b></div><div><ul><li>Zeng, D., H. Chen, R. Lusch and S. Li (2010): Social Media Analytics and Intelligence, IEEE Intelligent Systems (25:6), pp. 13-16.</li><li>Agrawal,D., Budak C., El Abbadi A. (2011): Information Diffusion In Social Networks: Observing and Influencing Societal Interests. In Proceedings of VLDB’11.</li><li>Leskovec, J.(2011): Social media analytics: tracking, modeling and predicting the flow of information through networks. In Proceedings of WWW (Companion Volume) 2011, pp. 277-278.</li><li>Nagarajan, M., Sheth, A., Velmurugan, S. (2011): Citizen Sensor Data Mining, Social Media Analytics and Development Centric Web Applications. In Proceedings of the 20th international conference companion on World wide web (WWW’11), pp. 289-290.</li><li>Bruns, A. & Stieglitz, S. (2012): Quantitative Approaches to Comparing Communication Patterns on Twitter. Journal of Technology in Human Services (30:3-4), pp. 160-185.</li><li>Stieglitz, S. & Dang-Xuan, L. (2012): Social Media and Political Communication - A Social Media Analytics Framework. Social Network Analysis and Mining (SNAM), DOI 10.1007/s13278-012-0079-3, Springer.</li></ul></div><div><br></div></div></div></div><div apple-content-edited="true"><span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; font-size: medium; "><span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-variant: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; "><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-variant: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; "><span class="Apple-style-span" style="border-collapse: separate; font-variant: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; "><span class="Apple-style-span" style="border-collapse: separate; font-variant: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; "><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><span class="Apple-style-span" style="border-collapse: separate; font-variant: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; "><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div><font class="Apple-style-span"><div style="font-style: normal; font-family: Helvetica; font-size: medium; font-weight: normal; "><strong><font class="Apple-style-span" color="#747474">--</font></strong></div><div><font class="Apple-style-span" color="#747474"><font class="Apple-style-span" face="HelveticaNeue-Medium" size="2">Prof. Dr. Stefan Stieglitz</font></font></div><div><font class="Apple-style-span" color="#747474"><font class="Apple-style-span" face="HelveticaNeue-Medium" size="2"><br></font><font class="Apple-style-span" face="HelveticaNeue-Light" size="2">Research Group for Communication and Collaboration Management</font></font></div><div><font class="Apple-style-span" color="#747474"><font class="Apple-style-span" face="HelveticaNeue-Light" size="2"><br></font></font></div><div><font class="Apple-style-span" color="#747474" face="HelveticaNeue-Light"><small><font class="Apple-style-span" size="1">University of Muenster, Department of Information Systems<br>Leonardo-Campus 11, 48149 Muenster, Germany</font></small></font></div><div><font class="Apple-style-span" color="#747474" face="HelveticaNeue-Light"><small><font class="Apple-style-span" size="1"><br>T: +49 (0) 251 83 38 115<br>F: +49 (0) 251 83 28 002<br>E: <a href="mailto:stefan.stieglitz@uni-muenster.de">stefan.stieglitz@uni-muenster.de </a><br><br>Web: <a href="http://www.wi.uni-muenster.de/kuk">www.wi.uni-muenster.de/kuk</a><br>Twitter: <a href="https://twitter.com/wikuk">@wikuk</a> | Facebook: <a href="https://www.facebook.com/wikuk">fb.com/wikuk</a> | YouTube: <a href="http://www.youtube.com/wikukunims">youtube.com/wikukunims</a></font></small></font></div></font></div></div></span></div></span></span></span></div></span></span>
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