Many scholars discuss the role of social media in the context of social movements, but there remain major disagreements regarding the precise role that social media plays. One area that deserves more in-depth study is the affordances of social media for constructing collective identity. This article examines the case of an Iranian women's rights campaign page on Facebook, "My Stealthy Freedom," using an analysis of textual and visual content. The article examines how online campaign pages on Facebook contribute to the formation of collective identity and the construction of a campaign narrative. Following the analysis, the authors discuss how photobiographic campaigns-social media users sharing personal photos and adjoining personal narratives in support of a cause-illustrate two affordances of social media for construction of collective identity: affordances for discourse and affordances for performance. Affordances for discourse contribute to the collective action framing process through sharing of grievances and collectively negotiating meaning. These affordances also contribute to a collectively and incrementally constructed narrative by sharing personal stories that resonate with the group. Affordances for performance focus on the enactment of protest through transgressive photobiographies deliberately staged to convey the movement message to broader audiences. Here, transgressive photobiographies are defined as modular performances that can be adopted for the repertoires of contentious politics through protesting of laws and norms, such as the mandatory hijab. These transgressive performances create group solidarity through engagement in risk, thereby contributing to the formation of group identities.

In this paper, we report the results of our work on automated detection of qanat shafts on the Cold War-era CORONA Satellite Imagery. The increasing quantity of air and space-borne imagery available to archaeologists and the advances in computational science have created an emerging interest in automated archaeological detection. Traditional pattern recognition methods proved to have limited applicability for archaeological prospection, for a variety of reasons, including a high rate of false positives. Since 2012, however, a breakthrough has been made in the field of image recognition through deep learning. We have tested the application of deep convolutional neural networks (CNNs) for automated remote sensing detection of archaeological features. Our case study is the qanat systems of the Erbil Plain in the Kurdistan Region of Iraq. The signature of the underground qanat systems on the remote sensing data are the semi-circular openings of their vertical shafts. We choose to focus on qanat shafts because they are promising targets for pattern recognition and because the richness and the extent of the qanat landscapes cannot be properly captured across vast territories without automated techniques. Our project is the first effort to use automated techniques on historic satellite imagery that takes advantage of neither the spectral imagery resolution nor very high (sub-meter) spatial resolution.

The fallacy of premature designations such as “Iran's Twitter Revolution” can be attributed to the empirical gap in our knowledge about such sociotechnical phenomena in non-Western societies. To fill this gap, we need in-depth analyses of social media use in those contexts and to create detailed maps of online public environments in such societies. This paper aims to present such cartography of the political landscape of Persian Twitter by studying the case of Iran's 2013 presidential election. The objective of this study is twofold: first, to fill the empirical gap in our knowledge about Twitter use in Iran, and second, to develop computational methods for studying Persian Twitter (e.g., effective methods for analyzing Persian text) and identify the best methods for addressing different issues (e.g., topic detection and sentiment analysis). During Iran's 2013 presidential election, three million tweets were collected and analyzed using social network analysis and machine learning. The findings provide a more nuanced view of the political landscape of Persian Twitter and identify patterns in accordance with or in contrast to those identified in the English-speaking Twittersphere around the 2013 presidential election. Persian Twitter was dominated by micro-celebrities, whereas institutional elites dominated English discourse about Iran on Twitter. The results also illustrate that Persian Twitter in 2013 was predominantly in favor of reformists. Finally, this study demonstrates that sentiment analysis toward political name entities can be used efficiently for mapping the political landscape of conversation on Twitter.

The spread of the Internet coupled with knowledgeable users has led to the use of digital media as a tool for advocacy and activism. Building on theoretical foundations of eventful histories and digital formations, this article investigates the interrelated nature of contentious politics and digital technologies. Our analysis documents the eventful history of changing digital repertoires of contention in the context of messaging, blogging, and social networking sites in Iran. We argue that investigating single moments of protest offers only snapshots of how digital technologies are used in contentious politics, and entails the risk of focusing on a single platform rather than the mosaic of online and offline repertoires. We demonstrate that documenting event histories challenges the assumptions of the emancipatory nature of a specific technology by revealing the changing efficacy of repertoires during different moments of contention; therefore, we should avoid assigning stable causal relations between digital technologies and the democratization processes of societies.