The article reviews the book "Alone Together: Why We Expect More From Technology and Less From Each Other," by Sherry Turkle.

Alone Together: Why We Expect More from Technology and Less from Each Other.

What is Twitter

The Net Delusion: The Dark Side of Internet Freedom by Evgeny Morozov New York: Public Affairs, 2011 409 pages $16.99 [ILLUSTRATION OMITTED] In January 2010, Secretary of State Hillary Clinton gave a highly touted speech on Internet freedom in which she stated, "The freedom to connect is like the freedom of assembly, only in cyberspace. It allows individuals to get online, come together, and hopefully cooperate. Once you're on the Internet, you don't need to be a tycoon or a rock star to have a huge impact on society." Evgeny Morozov, in his book The Net Delusion, takes great issue with the implication, however, that the so-called "Arab Spring" and "Twitter Revolution" were caused by unfettered access to the Internet. Instead, Morozov, a research academic, provides a cautionary tale about what he argues is any attempt to establish a monocausal relationship to meaningful political change (especially when that single focus is information technology). The book opens with a discussion of cyber-utopianism and Internet-centrism--mind-sets that focus on the positive "emancipatory" aspects of Internet communication while ignoring the downsides. The argument throughout centers on nation-state policy, or lack thereof, that attacks the "wicked" problem of authoritarianism by, as a colleague of mine has dubbed it, "wiring the world." Morozov, expectantly, but importantly, cites the hedonistic world portrayed by Huxley and the "Big Brother" world of Orwell to consider both the proactive and reactive approaches to Internet freedom by authoritarian regimes. Interestingly, he notes that there is often a mix of both. Such regimes certainly use the anonymity and openness of the Internet to spy on their people and shutdown undesirable sites. But there is also a subtle approach that belies the jackboot on the keyboard methodology. While China may be known more for suppressing the Internet and for employing the masses to counter antiregime rhetoric, Russia imposes no formal Internet censorship. It relies on entertainment (porn is specifically cited) to soothe the masses, assuming that given options for political discourse and anything else, most opt for "anything else." Hitler would understand. And in nations where freedom is not widely understood from a western perspective, any bit of additional mindless diversion may be viewed as liberty by the populace. Perhaps most importantly, Morozov rails against social media determinism as driving the end of authoritarianism, labeling it "an intellectually impoverished, lazy way to study the past, understand the present, and predict the future." He does not dismiss the value of Facebook and Twitter to quickly mobilize like-minded individuals. He notes as well that the development of that very like-mindedness is complex and potentially can be manipulated by authoritarian governments using the same Internet freedom. …

The Net Delusion: The Dark Side of Internet Freedom

Nationality: American Education: Ph.D. in Sociology and Personality... Alone Together: Why We Expect More from Technology â€¦ https://www.goodreads.com/book/show/8694125-alone-together Start by marking â€œAlone Together: Why We Expect More from Technology and Less from Each Otherâ€ as Want to Read: ... Unfortunately I am no closer to telling you why we expect more from technology & less from each other than I was before I read this book. One of the main things that bothered me about this book was that, even though I was really interested in these â€¦

Alone Together: Why We Expect More from Technology and Less from Each Other

1 July 7, 2010, marks the fifth anniversary of the 2005 terrorist attacks on London’s Metro system. In 2005, terrorists launched a coordinated attack against London’s transportation system, with 3 bombs detonating simultaneously at three different Metro stations and a fourth bomb exploding an hour later on a city bus. In all, there were 52 victims in these bombings with an additional 700 injuries resulting. The four terrorists who executed the attacks were killed in the explosions.

National Consortium for the Study of Terrorism and Responses to Terrorism

Massive Open Online Course (MOOC) has developed rapidly in recent years. However, the low satisfaction and the feelings of loneliness tend to cause more dropouts. A solution called Adaptive Recommendation for MOOC (ARM) is proposed aiming at the problem. Traditional MOOC recommendations are usually on the feature of interest. Among the recorded MOOC data, new recommendation features are selected for better balance on satisfaction. ARM trades off features adaptively according to the learner's requirement of satisfaction. Collaborative Filtering provides explicit information of similar learners and supports Collaborative Learning for less loneliness. ARM creatively combines Collaborative Filtering and time series to improve the recommendation accuracy. Specifically, Hawkes point process is improved to model the motivate and demotivate effect of score for future learning. Experiments with real‐world data show the accuracy of the ARM in recommendations and improvements in the dropout rate.

Adaptive recommendation for MOOC with collaborative filtering and time series

The fragility and interconnectivity of the planet argue compellingly for a greater understanding of how different communities make sense of their world. One of such critical demands relies on comparing the Chinese and the rest of the world (e.g., Americans), where communities' ideological and cultural backgrounds can be significantly different. While traditional studies aim to learn the similarities and differences between these communities via high-cost user studies, in this paper we propose a much more efficient method to compare different communities by utilizing social media. Specifically, Weibo and Twitter, the two largest microblogging systems, are employed to represent the target communities, i.e. China and the Western world (mainly United States), respectively. Meanwhile, through the analysis of the Wikipedia page-click log, we identify a set of categorical `hot events' for one month in 2012 and search those hot events in Weibo and Twitter corpora along with timestamps via information retrieval methods. We further quantitatively and qualitatively compare users' responses to those events in Twitter and Weibo in terms of three aspects: popularity, temporal dynamic, and information diffusion. The comparative results show that although the popularity ranking of those events are very similar, the patterns of temporal dynamics and information diffusion can be quite different.

https://www.cs.pomona.edu/~mwu/papers/2014-HT-Twitter-Weibo.pdf

Comparing the pulses of categorical hot events in Twitter and Weibo

The proliferation of social media is bringing about significant changes in how people make sense of their world and adopt new information. However, social, cultural and political divisions continue to separate users and information into different social media systems. Twitter and Facebook, for example, are strictly forbidden in mainland China. As a result, 21.97% of all world-wide Internet users are thus excluded from participating in these platforms. In this study, we investigate whether the dynamics of information diffusion, modeled as the adoption patterns of topical hashtags, differ between the communities of the mentioned social media sites as a result of this separation. Specifically, we compare Weibo and Twitter, the two largest micro-blogging sites serving respectively the Chinese population and the rest of the world, by exploring the similarities and differences of how their respective users adopt new information. By leveraging sophisticated community detection algorithms and heterogeneous graph mining methods, we investigate and compare how the different characteristics of these communities influence information diffusion and adoption. Experimental results show that while community-specific information influences topic diffusion and adoption in both environments, novel features, extracted from heterogeneous graph based communities, have a greater effect on Weibo information adoption than Twitter. We also find that users sharing hashtags is an important factor in information diffusion on both Twitter and Weibo, whereas user mentions are important for Weibo, but less so for Twitter. Overall, we conclude that Weibo and Twitter differ sharply in how their users adopt information in response to similar factors.

Comparing Community-based Information Adoption and Diffusion Across Different Microblogging Sites

Mining the popular SMS messages in a short period of time is very valuable. However, traditional OLAP-based mining method is not suitable for this very large scale dataset. In this paper, we present a mining approach based on Map-Reduce parallel framework: Firstly, original dataset is pre-processed and grouped by the senders' mobile numbers. Secondly, we do a transformation to regroup the dataset by the short content keys, and then extract the popular messages according to the count of different senders which have the same key. Furthermore, we propose a sentence similarity computation method and a novel Forward Merging and K-Neighbor Checking algorithm to merge the similar messages semantically. Experimental results show that the final dataset of popular messages is very small with high sending coverage ratio, and can meet the real requirements.

Large-Scale SMS Messages Mining Based on Map-Reduce

The Global Terrorism Database (GTD) is the most important dataset in counter-terrorism domain. Existed studies based on GTD focused on terrorism influences, data statistics and visualization, and terrorism event mining such as classification and clustering. In this paper, we build a terrorism knowledge graph(TKG) from GTD and Wikipedia. Compared with GTD, TKG enhaced the organizations of terrorism entities and relationships, and enriched the description by attatching Wikipedia knowledges. Therefore, TKG can better the understanding of terrorism attacks for both human beings and machine processing like graph mining and knowledge reasoning.

Tian Xia

Papers

Adaptive recommendation for MOOC with collaborative filtering and time series

Comparing the pulses of categorical hot events in Twitter and Weibo

Comparing Community-based Information Adoption and Diffusion Across Different Microblogging Sites

Large-Scale SMS Messages Mining Based on Map-Reduce

Building Terrorist Knowledge Graph from Global Terrorism Database and Wikipedia