Social Computing 2
- 1 Readings
- 2 Reading Critiques
- 2.1 Zihao Zhao 23:41:44 11/8/2015
- 2.2 Vineet Raghu 23:26:52 11/11/2015
- 2.3 Samanvoy Panati 0:56:10 11/14/2015
- 2.4 Ameya Daphalapurkar 15:35:17 11/14/2015
- 2.5 Manali Shimpi 23:18:36 11/14/2015
- 2.6 Priyanka Walke 1:04:04 11/15/2015
- 2.7 Ankita Mohapatra 11:20:35 11/15/2015
- 2.8 Adriano Maron 16:28:24 11/15/2015
- 2.9 Long Nguyen 19:07:25 11/15/2015
- 2.10 Matthew Barren 19:46:24 11/15/2015
- 2.11 Darshan Balakrishna Shetty 21:26:17 11/15/2015
- 2.12 Chi Zhang 23:28:50 11/15/2015
- 2.13 Lei Zhao 0:17:47 11/16/2015
- 2.14 Xinyue Huang 2:29:18 11/16/2015
- 2.15 Zinan Zhang 6:18:25 11/16/2015
- 2.16 Shijia Liu 6:29:16 11/16/2015
- 2.17 Mingda Zhang 8:48:48 11/16/2015
- 2.18 Mahbaneh Eshaghzadeh Torbati 8:55:17 11/16/2015
- 2.19 Kent W. Nixon 9:01:27 11/16/2015
- Predicting Tie Strength With Social Media Gilbert, E., Karahalios, K., Proceedings of CHI 2009, pp. 211-220.
- Design Lessons from the Fastest Q&A Site in the West, Lena Mamykina et al, in Proc of CHI 2011.
Zihao Zhao 23:41:44 11/8/2015
“Predicting Tie Strength With Social Media” is a literature which bridges the gap between the theory and practice of using the social media to accurately predict the tie strength. This is a very novel research before 5 years because the social media has been grown into a very powerful tool which has greatly influenced our daily life. Mark Granovetter introduced the tie strength in his paper “The Strength of Weak Ties”. I have been using social media applications for more than 5 years and I can strongly feel it’s inner power with ties. The people I contact most and I see most of their status will influence me. When I first realize the aim of the paper, I thought about the main work is how to define the tie and quantify it. It is hard to quantify it because their are so many factors that can influence the ties. To solve this problem, this paper first using the original idea from Granovetter and to adopt four factors: amount of time, intimacy, intensity and reciprocal service. However, it is not enough in the social media environment. Maybe we can say that we can take the convenience of social media to create some new dimensions. At pastl, there are a lot of limitations to do user study because the users had to recall their friendship. However, with the social media, we can easily analyze the data from the history of the relationship of the users, and we can take advantage of long friend lists and rich interaction histories. Thus the paper came to 74 Facebook variables as potential predictors variables and the author took the advantages of Facebook’s breadth while selecting variables that could carry over to other social media. After determining the variables, the next topic should be fix their weight to the whole tie value. To address this problem, the paper modeled tie strength as a linear combination of predictive variables because the linear model can help to take advantage of the full dataset and explain the results once it is built. And the research also come across the common obstacle for ego-centric designs that the observations within a participants were not independent.———————————————————————————————————— “Design Lessons from the Fastest Q&A Site in the West” is a literature which analyze the data of Stack Overflow and the interview for the founders to find out the reason why Stack Overflow is so successful. The success of Stack Overflow is great because the result turned out that most of the questions were answered correctly(90 percent) and most of the questions were answered in 11 minutes and the registers were over 110k. I love this paper because in some sense it is more practical in the industry part. Thus I treat it as an inference and a brochure and as if I am going to establish a Q&A forum. The factors for its success will not go beyond the following three: 1)Making competition productive, the tight focus on technical answers enabled by the Q&A format and a voting system created a strong alternative to the existing software forum. One key for the success or the Stack Overflow is that the founders themselves are active software developers who know the technique questions and their format. 2)Credibility in the community. An obvious question to Q&A forum design is credibility and reliable of the answers. One possible solution is to make the registers gain more credits on the credibility which means their answers will be more reliable in the future. And also, mentioned by Prof. Wang, that it will be more meaningful to get the authentication from the social media like Facebook because the answerers will be labeled expert if he answered more correct questions and it can also prevent some malicious fault answers. 3)Evolutionary approach to design. The feedback is very important. What can motivate the users to answer the questions still remain a critical question in the Q&A forum design. What the Stack Overflow did was to give some reward and prioritize features to the answerers after a few iterative designs. Also there exists 5 class questions that the Stack Overflow cannot answer. Those questions on one hand lack the motivation for the users to answer if the questions are tedious to answer or have some obstacles for the users to answer like some obscure technologies for which there are few users on the other hand.
Vineet Raghu 23:26:52 11/11/2015
Predicting Tie Strength with Social Media This paper presents a predictive model that takes social media data and predicts strong and weak ties between friends in the dataset. The authors argue that the ability to have a reliable prediction of strong and weak ties could be very useful for social media purposes as sites could use this information for security or relevance filtering of information. The proposed model is fairly accurate, correctly classifying strong versus weak ties 85% of the time. Next, the authors discuss the predictive variables that they utilized through user questions as well as through Facebook profiles, including detailed descriptions of how these variables were gathered for each user. Then, the authors describe their model which is a linear model that predicts the tie strength of a user with their ith friend using the predictive variables as well as pairwise interactions between the variables that had at most 10% missing values. It also takes into account the network structure, since the tie strength of a friendship can also depend on the tie strength of mutual friends in the network. Overall, the model appears to be a very good step in predicting tie strength in the future. One critique I had of the paper was the fixed ordering of tie strength questions. Perhaps dividing the friends that must be rated into blocks and then randomizing the ordering over blocks could mitigate the confusion caused by randomization while still preventing fatigue from being a factor for all individuals. The only other critique of the paper is something that the authors themselves slightly acknowledge at one point which is that this social network that is being examined is very specific in that all the members are from one University community. Thus, highly predictive variables may not generalize well to other studies. A follow up to this paper should definitely try to examine various communities or pull users from various domains perhaps through an online approach. -------------------------------------------------------------------------------------------------------- Design Lessons from the Fastest Q & A Site on the Web The goal of this paper is to examine the website Stack Overflow, which is a highly successful question and answer website for software developers. In doing so, the authors hope to extract general principles for these types of websites that can be applied to other forums. However, the authors argue in this paper that the primary reason for Stack Overflow’s success is due to the fact that the design team is highly involved in the community that the website is aiming to benefit, whereas this is typically not the case for these types of sites. Next, the authors discuss in detail how well Stack Overflow performs and usage properties of the website such as the average response time per question, classification of users into groups based on site usage, and the type of questions that may or may not be well supported by Stack Overflow’s format. Afterwards, the authors discuss major findings from their qualitative survey of various types of Stack Overflow users. First is the focus on information as opposed to conversation, which gives users quick and accurate access to specific information that answers their question. In addition, this focus allows voting schemes to work, because when there’s a conversation occurring, users have to read every comment to understand the conversation at hand diminishing the usefulness of voting which allows users to read only specific useful comments. The next finding was particularly interesting that the reputation point system encourage positive behavior, but at the same time it encourage lightning fast answers that may not be as detailed or useful, since they will provide quicker reputation points. Finally, the founders credibility themselves allowed them to quickly gather a critical mass of users which is necessary for a social site such as this. Likewise, their reputation allowed them to have support for many of the design decisions that they made. I think that the most important finding is the evolutionary nature of Stack Overflow that allows it to continue to adjust to the major challenges that it faces. The meta site enables the developers to continuously monitor the most important changes that needs to be make. Overall, this paper was a very beneficial study of the successes of Stack Overflow, and it appears that the major influence towards its success is the nature of the founders of the site. Being heavily involved in the community allows the developers to gather support quickly and to maintain support in the face of difficult decisions. This coupled with a good design that promotes the spread of information allows for a successful website.
Samanvoy Panati 0:56:10 11/14/2015
Critique 1: Design Lessons from the Fastest Q&A Site in the West The paper describes Stack Overflow, a popular Q&A site for programmers and software engineers and analyzes its design and evolution which made it successful. First, the authors gave some statistics which show that Stack Overflow (SO) is indeed successful. Its median time to answer a question is 11 minutes which seems to be the normal time taken by a person to answer a technical question. Also, the monthly visits to this site are greater than 7 million. Then they present the three factors they believe were instrumental for the success of Stack Overflow. First one is “making competition productive”, which was done by making some game mechanics that led the users to be active. Second one is “credibility”, which was achieved by special status and badges to the dedicated users. The last one is “Evolutionary approach to design” which was achieved by the prompt reaction of the design team for the feedback they get through the meta site. Then they illustrate some analysis methods which are found by analyzing many Q&A sites. Interesting one among them is extrinsic motivators. Adding monetary rewards can transform user’s sense of system from social interaction space to more formal transaction space. SO implemented this by giving special reputation and special badges to more active users. The problem with the forums is that it is difficult to locate relevant posts in them. The problem with social Q&A’s is that there is a tradeoff between size and proximity. SO has a meta site where users posts some design suggestions and other users can vote for them and the votes increase its priority. Then some statistics of SO are given along with the comparison with the sites like KiN and Yahoo answers. SO performed very well among its peers. One tradeoff in SO that is mentioned is questions about obscure technologies might not be answered well as there will be less number of users in that domain. The main challenge found as seen in discussions is, the novice users take very long time to get potential status. This paper is very interesting and it very well describes the factors and features those are responsible for the success of Stack Overflow and also gives some motivation for new ideas. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Critique 2: Predicting Tie Strength with Social Media This paper illustrates the concept of tie strength and its mapping to the social media. The authors start with the definitions of weak and strong ties and the relationships that fall into these categories. The people whom we trust and share our emotions fall into the strong ties category and the people we have less acquaintance with fall into the weak ties category. Then they present the tie strength model along with its seven dimensions. The dimensions proposed by different authors are combined and finalized as into seven dimensions which are intensity, intimacy, duration, reciprocal services, structural, emotional support and social distance. Then a research study is conducted on 35 participants who are from the authors’ university and includes both teachers and students. They were asked questions about their relationship with some friends from their facebook profile. 74 variables were identified as potential predictors of tie strength. Some interesting variables are inbox thread depth which measures the number of messages exchanged privately and wall words exchanged which measures the number of words exchanged through the posts on their walls. Then they explore the interactions between the dimensions of tie strength and give many stats. Finally, a few follow-up interviews are conducted to find out some details about the friendships that are difficult to predict. An interesting example mentioned is that a student gave rating to a professor as high because he is friendlier than many of his facebook friends even though he talks to him less frequently. Education difference predicts tie strength positively. But some variables like inbox thread depth, which are thought to be positive attributes to tie strength, affected it negatively. Among the variables, intimacy made the greatest contribution to tie strength which was 32.8%. This paper started with nice introduction of the concept and variables but it is somewhat bland in the statistics part but it gained momentum towards the end. As a conclusion, it gave a good perspective of social media and its future implications.
Ameya Daphalapurkar 15:35:17 11/14/2015
The paper titled ‘Predicting tie strength with social media’ is about analyzing the bar or level of friendship between two individuals on Facebook. The level of friendship is judge either as a strong or weak tie. The example of this classification can be that family is a strong tie, and new friends or individual with not that close acquaintance is a weak tie. The measurements to define the tie strength vary a lot. The paper talks about seven dimensions though and some manifestations. The user data was collected by a Firefox plug in named Greese monkey. User were asked to rate their friends and then this information was further used to study and analyze to understand how the values fared. There were following variables which were used to calculate the results : predictive variables, intimacy variables, duration variables, reciprocal services variables, structural variables, emotional support variables and social distance variables. After applying the statistical methods, the inference was derived relatively. Although, in a personal opinion, I don’t think the system can be highly reliable. We talk to many close people on different social media platforms like WhatsApp, or even normal text messaging, so a relation cannot be derived from a singular platform. *************** The paper titled ‘Design Lessons from the Fastest Q&A Site in the West’ talks about Fastest Q&A Site, Stack Overflow. User interviews and statistical data analysis tools are the primary measures for the site to understand their success. To analyze the facts they used SO databases which are publicly available and they concluded that the reason for their functioning is high quality of answers and quick reply answers time. Making the competitive product, community credibility and evolutionary approach to design are the main factors of their success. Although better everywhere else, some of the data types are not that great in support by SO. Their Meta site also has an active base. Statistics concluded that active users posted more answers than the questions. Partial answers are often left uncorrected on SO. The problem should be addressed as soon as possible.
Manali Shimpi 23:18:36 11/14/2015
Predicting Tie Strength With Social Media: This paper presents a predictive model that maps social media data to tie strength. It showed 85% accuracy in distinguishing strong and weak ties. The paper used Facebook data for testing. The paper introduced seven dimensions viz. Intensity, Intimacy, Duration, Reciprocal Services, Structural, Emotional Support and Social Distance. These dimensions were under 4 proxies: possessing at least one mutual friend, recency of communication, interaction frequency, communication reciprocity. One of the interesting aspect of the paper was that the follow-up interviews conducted by the author about the friendships they had the most difficulty predicting. The paper took a popular web site to figure out why is was so famous and analyzed how these results can be used to improve other websites as well.---------- Design Lessons from the Fastest Q&A Site in the West: The paper analyzes the data of stack overflow to find out why stack overflow is successful. According to author , there are three factors which are critical to the success of stack overflow are making competition productive, credibility in the community and evolutionary approach to design. The author says that the Q and A structure on this website is unique that provides askers to quickly read the answers and also vote them. These vote intern encourages the answers to be clear, accurate and quick. The success of stack overflow is also because of reputation system. Some of the issues with stack overflow are, sometime some questions are left partially answered and are nor corrected later by any one. At the end I would like to say that this paper was inspirational for me since I am a user of this Q and A website.
Priyanka Walke 1:04:04 11/15/2015
Reading Critique on Predicting Tie Strength with Social Media This paper deals with predicting the social models, the ties and relationships using a prediction algorithm. This algorithm considers many variables using the existing research and also converts them into relevant social media. Moving on towards an interesting field of discussion that is to predict the behavioural patterns from the data collected from social media. This has been a major investment sector for many companies one of the best examples being the purchase of WhatsApp by Facebook. To extract the idea of how users think from the social patterns is very important for the success of any organization. It questions if the existing literature that suggests the 7 dimensions to determine a tie namely Intensity, Intimacy, Duration, Reciprocal, Services, Structural, emotional Support and Social Difference is sufficient enough in order to predict the thinking pattern with the use of social media and the drawbacks of using such an approach. Using the count of the author’s Facebook variables, it is definitely not an easy process. His predictions is about 87% accurate which is appreciable considering the complexity of the variables involved and their relationships. Such models definitely involve huge implications like privacy and control but then it loses out on an unexpected behaviour of people thereby leading to new connection formation. Next drawback is that the users were asked to rate some of their friends randomly and this particular data was used to predict the accuracy of the model. Since, this is prejudiced view of a tie between views of 2 people, it is for sure that one might consider his/her own view strong while the other might not. This kind of discrepancy will remain if the data is not collected from both the peers. It can be summarized by saying that the paper is quite deterministically written with clear concepts. ================================================================= Reading Critique on Design Lessons from the Fastest Q&A Site in the West This paper talks about Stack Overflow, which is a Q & A site. It majorly states the reasons for the success of this site as compared to other sites. The main intuition of this paper was to find those design principles that made Stack Overflow one of the most referred sites for technical queries along with its impact on HCI and CSCW researches that are being made. One such effect is that Stack Overflow uses an iterative prototyping design with continuous feedback from the users, which is one of the major reasons for its success. However, implementing this in an application oriented environment is one of the major challenges for the researchers. This paper seems like combining concepts from the previously read papers read to create a great platform. An important fact in case of the groupware paper discussion on initial critical mass is that the founders already had an existing substantial following and also a reputed position in the community. Speaking about the social behaviour of the users of Stack Overflow, that they are divided into groups like regular, shooting stars and guest. Also, their involvement is periodically evaluated with the performance of the site compared to the other websites. The authors here provides an alternative solution however, the Stack Overflow has the fastest response as well as acceptance time, along with the valid answer given the top priority by voting. Since we learn a lot about the design practices that can help us in the field of social computing. Maybe, we can create a combination of it with stack Overflow to create a stable platform that may not require continuous monitoring.
Ankita Mohapatra 11:20:35 11/15/2015
Predicting Tie Strength With Social Media In this paper, a predictive model that maps social media data to tie strength is presented. The model distinguishes between strong and weak ties. The author uses several selected parameters to predict how strong the social tie is, predictive variables including intimacy, intensity, duration, emotional support etc. The result turns out to be very accurate with over 85% accuracy. For the friendships they had the most difficulty predicting, the authors conducted follow-up interviews to understand the reasons. Strong ties are the people you really trust, share the same social circles and most like you; weak ties, conversely, are merely acquaintances that provide access to novel information that not circulating in the closely knit network of strong ties. One uniqueness of this work is the leverage of social media. Participants don't have to recall, such that the problem of retrospective informant accuracy is avoided. The rich information of friend lists and interaction histories can also be utilized. It can also potentially benefit the users of social media, too. A lot of independent variables are selected as independent variables for the black box. Variables are classified into 7 categories: intensity, intimacy, duration, reciprocal, structural, emotional support and social distance. The dependent variables are 5 tie strength questions, with each one variable of continuum levels. A linear model is used to predict the relationship between the independent variables and one of the dependent variables. An interesting point made in this paper is the explanation of why the model does not fit the last three questions as well as the first two; the authors infer that it might be resulted from participant fatigue. Randomizing can be a solution, but it may also annoy the participants. Hence the most interested question is prioritized. Another good point is that in order to understand the limitations, the authors conduct follow-up interviews about the friendships they had the most difficulty predicting. They pick the friends with the highest residuals and ask the participants about their relationship. The reasons are quite interesting to me; somehow they reflect the intrinsic complexity of human emotions. But as can be seen from the examples, the model tend to underestimate how strong the tie is. I think it implies that good friends do not necessarily interact in social media as intimate as in real life, which applies to me, too. This work seems to be interdisciplinary with social sciences. I am wondering how computer scientists can collaborate with social scientists to get the most of the abundant data generated everyday on social networks. ==================================== Design Lessons from the Fastest Q&A Site in the West This paper describes a popular Q&A site, Stack Overflow and analyzes factors in the site’s design and evolution that contributed to its success. The paper first gives a statistical data analysis of the entire SO corpus to understand usage patterns. The authors investigated answer time, user types, suitability for different question types, and possible extensions of the SO model to other domains. To ground this aggregate view in concrete user experiences, the authors also conducted a qualitative interview study with users and the design team. They believe that three factors are the main reasons SO succeeded: 1) Making competition productive; 2) Credibility in the community; 3) Evolutionary approach to design. Analyzing social networks sounds very interesting; however, I always have the concern of this kind of research: the process seems to be having the data first, and trying to get some insights out of it; not really like the "traditional" way to research that finding the question first and then collecting and analyzing the data. When got the raw data, most researchers will do simple statistics on it just like the authors did in this paper, to get the overview of the dataset; and if they see anything interesting, they try to dig deeper into it. Somehow I feel like this approach is not very straightforward to me. As seen in this paper, a dozen of statistics are presented to answer the question of "how well does stack overflow perform". From the total number of users, to answering time, to different types of users, the paper illustrates the overview of the SO dataset with the help of visual plots. Many of the plots exhibit power law distribution (big-tail) as expected as an online social networks. To better understand the driving factors behind these patterns the authors conducted a qualitative study of the community. They did interviews on the users, site designers and founders of SO. Three reasons contribute to SO's success: 1) founders’ tight involvement with the community, 2) highly responsive and iterative approach to design, and 3) a system of incentives that promoted desirable user behavior.
Adriano Maron 16:28:24 11/15/2015
Predicting Tie Strength With Social Media: In this paper, the authors present and evaluate a model to predict tie strength based on social media relationships. Thirty-five participants rated a random sample of friends in terms of: relationship strength, how comfortable when asking for a loan, how helpful if looking for a job, how upset if unfriended and how important to bring fried. The relationship between the participant and the friend was analyzed in terms of 74 Facebook variable, which were categorized in the seven dimensions of tie strength. The linear model proposed was able to accurately predicts the relationship strength. The follow up interviews performed by the authors provided extra information about the relationship strengths that could not be predicted. This helps when analyzing more subjective aspects that can not be directly inferred by Facebook parameters. This paper approached a initially very complex and abstract problem in a objective way, what resulted in a great results for an area that has increasingly significance in our daily lives. ================================================== Design Lessons from the Fastest Q&A Site in the West: This paper addresses investigates the key elements that may result in the success of an CSCW service using the Stack Overflow (SO) website as a study case. SO was chosen due its success in providing answers to most of the software/programming problems posted within a very small waiting time. Many are the factors that can contribute to such success: large group of users with similar problems, large group of expert users, user's reputation, strict guidelines about how to ask/answer questions, and more. However, one of the most important factors discovered was that the involvement of the design team in the control and debate within the community. The authors provided a qualitative study to back up the claiming about the key elements, and this contribution is important because it exposes the relevant factors that can contribute to the success of a collaborative tool. Besides, such strategy can be used to create similar services in different domains.
Long Nguyen 19:07:25 11/15/2015
Predicting Tie Strength With Social Media: This paper presents a model to quantify social tie strength between friends on social media, specified in facebook. Tie strength can help many ways in social network, like privacy controls, friend introductions and information prioritization, however I hope authors can talk more details about how this information could be treated to design new social network if possible. One of the contribution of this paper is the way authors use script to collect data, however I believe it's not enough since the script was run in client side. In the evaluation, this model has an accuracy of high 85% and I think it's enough for pratical implementation in many applications. ------------------------- Design Lessons from the Fastest Q&A Site in the West: the second paper discuss about Stack Over Flow website and reasons why it's so successful. I believe as a CS student, one should at least go to this website several times to find debugging method and technique stuffs. SO has short thread length, however it's very concise and useful, espeically when they allow comments on each solution. This help users a lot in order to find the correct answer for their own problems. The authors point out three main reasons leading to its success nowadays: making competition productive, credibility in the community and evolutionary approach to design. I agree with all three reasons and find the writing very nice since author supports his idea with lots of graphs.
Matthew Barren 19:46:24 11/15/2015
Summary of Design Lessons: The authors’ look at Stack Overflows performance to address a niche of community needs compared to other question and answer sites. They examine Stack Overflow through a qualitative research study by interviewing the websites users. The authors rightly point to the success of Stack Overflow by noting several key factors. For example, extrinsic badge systems, which Stack Overflow uses as well. One of the most important parameters to the sites success is the scope of output. Stack Overflow is dedicated to objective answers for one particular subject, software development and coding. Unlike many other websites, Stack Overflow discourages conversation, and in its place, encourages quick, accurate, and concise answers. This particular factor reduces the opportunity for extreme remarks, and keeps users focused on question and response actions. Removing the opportunity for subjectivity leads to easier fact checking from users. The answer to provide a solution for a particular piece of code can be quickly tested for correctness. Additionally, Stack Overflow’s community is foundationally built on expert users, which allows questioners to have their questions accurately answered. A confounding variable that could be driving the success of Stack Overflow is the directness of feedback users receive from their own computer system. For example, an individual compiles a particular piece of code, and an error is produced. The user cannot figure out the means to fix the error, and thus, turns over the code and error output to Stack Overflow. This provides an easy mapping for an individual to enter the conversation and provide a quick answer. Additionally, since the questions follow a systematic pattern, Stack Overflow can take advantage of this to easily manage the question archive. This study was performed in 2011. In 2015, Stack Overflow may not be the same environment it once was. For example, the number of active questions entering the site may not be as frequent as they previously were because of the large archive of questions that have already been produced. For example, I have never posed a question on Stack Overflow. Instead, I will typically Google a question, and a Stack Overflow thread with that question has already been answered. Summary of Predicting Tie Strength with Social Media: Gilbert and Karahalios perform a study on relationship ties between users on Facebook. Their study examined a number of different dimensions related to relationships, such as intensity, intimacy, and duration. Gilberts’ and Karahalios’ research on predicting relationships is an interesting examination of perceiving the connections between individuals through displayed interactions. There results found that intimacy was the greatest contributor to predicting relationships. The variables and means through which they collected this data is a unique fit model. For example, one variable to learn about intimacy relates to the words included in conversations. Although this model is a unique examination, the authors point out that the difference between users’ social media habits is greatly varied. One example is the difference found between public and personal conversations. It is common to see some engage in more public posting than others, but it is incorrect to assume that this interaction is a sign of a stronger relationship. In actuality, it means that this user more actively uses this particular feature, but it provides little to no context of the relationship between the two individuals in question. Additionally, age range will dictate how individuals use social media. For example, college age students will have more topical conversations with individuals who are at their university than friends from home. This may not necessarily translate into stronger ties between those individuals. Predicting social relationships can be a gateway to many more interesting findings related to users through data mining social interactions. For example, strong relationship ties may provide deep conversations to demonstrating a users emotion at a particular point in their life. If computers can predict emotional fluctuations of individuals, an interaction can be initiated by the machine to try to assist the individuals current state. Additionally, understanding relationship ties on social media provides a pathway to understand how users utilize social media with varying levels of relationship intensity.
Darshan Balakrishna Shetty 21:26:17 11/15/2015
Predicting tie strength with social media: This article presents a quantitative study about the significance of tie strength in social networks. The authors generalize the argument through an extensive user-study which tie's strength is a very important factor that affects user behavior in social networks. Previous work on the same field has failed to measure the strength of ties in depth. However, the boom of social networks that we are experiencing over the past decade has brought this matter to the attention of researchers. By examining user behavior the authors succeed in gathering data about the effect of social ties in social networks and manage to break it down in different factors. Also, the authors manage to build a prediction model for analysing user behavior based on a tie's strength. This paper is easy to read and the authors' motivation is clear from the beginning. "Social Mining" is a very important field that affects many research areas of Computer Science. ------------------------------------------------------------------------------------------------------------------------------- Design Lessons from the Fastest Q&A Site in the West: This article mainly focuses on the social phenomena and the evolution of one of the most popular Q&A sites for developers “ StackOverflow ”. The author’s present statistical data gathered over 2 years of study, and conclusions made through interacting with community members of StackOverflow. Personally, I think most of us have used and are familiar with the StackOverflow and its dynamics. As StackOverflow is one of the pioneers in this type of community sites, the authors felt compelled to study the user behavior patterns and the incentives behind its design. From the point of view of the creators of StackOverflow, the authors reveal their intentions and how those affected the websites philosophy. Also, opinions and behaviors of active users are presented through statistical analysis of data and through interviews with the users themselves. Even though I did not understand the reason for presenting the statistical data in so much detail, the contribution of this paper is clear to me. Several behavior patterns have been demonstrated and the methods the creators of StackOverflow used to keep the user community active.
Chi Zhang 23:28:50 11/15/2015
Critiques on “Predicting Tie Strength With Social Media” by Chi Zhang. It is about the closeness of relationships. This research tried and predicted where on this spectrum a relationship lies based on the information on the social media site. The measurement of closeness has four dimensions, amount of time, intimacy, intensity, and reciprocal services. With these variables, researchers predict the actual strength of the relationship as indicated by the participants. In general, it’s quite an innovative idea. This is a very good paper, and it introduces the details about closeness of relationships. It’s actually providing us very good views to deeply understand current researches on human relationships. ----------------------------------------------------------- Critiques on “Design Lessons from the Fastest Q&A Site in the West” by Chi Zhang. This paper is about how StackOverflow becomes the fastest Q&A site in the West. StackOverflow provides better answers within a shorter period of time than other QA sites. And this comes from a reason. The authors discovered three contributors: it leveraged competition, formed a sense of trust within the community, and focused on evolving the design. The authors found that, there is a meta site to discuss the direction of the main site. The authors suggest that this helped keep the development cycle short, by carefully listening to its users. This style of design becomes the probable reason for StackOverflow being successful. The authors hope to utilize this kind of design. It’s a good observation paper, it takes into consideration all aspects of the success of StackOverflow, and tried to analyze on their specific style of designing Q&A site. It provides very insightful views and it’s a very interesting paper.
Lei Zhao 0:17:47 11/16/2015
<Predicting Tie Strength With Social Media> This paper introduces a model to predict people’s social tie strength based on social media, Facebook. Tie strength is a combination of time, emotional intensity and intimacy among people’s social community. There are seven dimensions to measure tie strength. The researchers asked two questions before experiments. One is can social media predict the tie strength. The second question is what are the limitations of tie strength prediction based only on social media. Based on the seven dimensions, the researchers choose 74 Facebook variables to predict tie strength. The result shows that intimacy makes the most contributions to the prediction. And they also found out social media can predict 80% relationships. This number could answer the two questions researchers asked above. Social media can predict tie strength, but it cannot predict all, which means there are limitations. So just relying on social media cannot predict all relationships. Researchers found out other relationships by interviewing the participants. And they complemented the social media and gave some suggestions to help social media to be more precise. ====================================== <Design Lessons from the Fastest Q&A Site in the West> This paper discusses about the factors behind the success of Stack Overflow, which is a very popular Q&A website. There are three major factors of the success of SO. First is making competition productive. SO relies heavily on community moderation. Active participants can vote questions and answers of others up and down. They are also able to close inappropriate question. Second factor is the credibility in the community. Even before SO, the founders had a combined readership of about 140000 people. SO has initial critical mass of users, which is a key point of social software system. The third factor is evolutionary approach to design. The SO design team adjust the design of the site and update the modification very often, which make their design iteration fast. In addition, designers have tight feedback loop with users. To HCI prospective, analysis the pattern of user interaction of SO could also help HCI researchers know more about common user groups and behaviors.
Xinyue Huang 2:29:18 11/16/2015
Predicting tie strength with social media The paper presented a predictive model that maps social media data to tie strength. The paper concludes by illustrating how modeling tie strength can improve social media design elements, including privacy controls, message routing, friend introductions and information prioritization. The paper introduced tie strength which is a combination of the amount of time, the emotion intensity, the intimacy (mutual confiding), and the reciprocal services which characterize the tie. Strong ties between employees from different organizational subunits can help an organization withstand a time of crisis. Yet, strongly tied coworkers are also the ones likely to create crisis by pushing for institutional change. There are four dimensions of tie strength: amount of time, intimacy, intensity and reciprocal services. The paper presented two questions like whether these dimensions or the combinations of the dimensions can predict the strength and what are the limitations of a tie strength model based solely on social media. The paper designed a method to answer the research questions. The research identified 74 Facebook variables as potential predictors of tie strength. For intensity variables, each Facebook users has a Wall, a public communication channel often only accessible to a user’s friends. For intimacy variables, to complement aggregate measures, we used the Linguistic Inquiry and Word Count (LIWC) dictionary to perform content analysis. For durable variable, the variable like days since first communication is a proxy for the length of the friendship. It measures time in the same way as Days since last communication. To capture reciprocal services on Facebook, Links exchanged by wall post measures the number of URLs passed between friends via the Wall, a common Facebook practice. For structural variables, Facebook allows users to join groups organized around specific topics and interests. There are some other variables such as emotional support variables, social distance variables, and demographic and usage variables. The paper has revealed a specific mechanism by which tie strength manifests itself in social media. Many paths open from here. Social media designers may find traction fusing a tie strength model with a range of social media design elements, including privacy controls and information prioritization. The follow-up interviews suggest in this field will find important new theoretical question in this work, as well as opportunities to use tie strength to make new conclusions about large-scale social phenomena. Design Lessons from the Fastest Q&A Site in the West The paper analyzes a Question&Answer site for programmers, Stack Overflow, that dramatically improves on the utility and performance of Q&A systems for technical domains. Tha paper believes on three factors that are critical to the success of SO. The first one is making competition productive. The second one is credibility in the community and the third one is evolutionary approach to design. The paper introduced some related work such as analyses of popular Q&A sites. There are some methodologies such as structural analyses capture aggregate use, qualitative and mixed method studies focus on individuals. Stack Overflow follows a common model of Q&A site design: users post questions, answer questions, comment, and vote on posts. To evaluate how well the stack overflow performs, there are some dimensions such as hundreds of thousands, but not millions of users. Most of questions are answered and most of them are answered multiple times. The answers are fast, most answer activity takes place in the first hours and answers have been fast since early on. There are some other dimensions such as that askers and answers overlap, frequent users post more answers than questions, four answer behaviors, community activists, shooting stars, low-profile users, and visitors. Questions receive dozens of views. While there are also some other disadvantages such as there are some questions that are not supported well by SO, meta site has a small, opinionated and active base and the SO model might extended to other domains. For qualitative study, the paper introduced the methods, findings, improving on forums through productive competition, credibility in the community and evolutionary approach to design.
Zinan Zhang 6:18:25 11/16/2015
Predicting Tie Strength With Social Media----- This paper mainly focuses on how to address the problem that predicting tie strength with social media. Since all the social media treat the users as trusted friends or stranger and no other relationship besides those two, the author intend to classify the relationship more specific. Indeed, nowadays, nearly everybody uses the social media. People use the social media as an important method to communicate with others. However, there are only two types of the relation between users: best friend or stranger. Best friends always can see what you post on your homepage and comment on it. On the opposite, the stranger can only watch your state or photos without any other communication, sometimes even cannot be able to see anything about your. This is not a good mechanism for a social media, because we cannot say everything to all of my friends. For example, I have some complain about one of my friends. I want to post it in my homepage and talk it with my best friends. But I do not want every friend to see it in case something unhappy happened. As well, I do not want the friend that I complain about seeing it. However, tie strength with all of my friend is the same. If I post the complain online, everybody would see it. But if there is a kind of mechanism that can distinguish the tie strength between my friends and I, the problem is never being a problem. I post my complains online and only my best friends can see it and talk with me about that in order make me feel better. In the paper, the author comes up with an idea that to make a quick query with you about the friends you add. And classify the tie strength between you and your friend according to the result of the query. This is a good try, but not good enough I think. It is not flexible enough. Actually, there is another mechanism called grouping is much better. The grouping is to divide your friends into different kinds of groups. When you post something on your homepage, you can select the certain group to see it. It avoid some unnecessary embarrassing to some extend. ------------------------------------------------ Design Lessons from the Fastest Q&A Site in the West------ This paper mainly talks about the author solving a problem from the Q&A site in the west. The observing and learning from the Q&A website, the author come up with a new method to address the old problem. It is fascinate. Actually, it is really an efficient and valid way to solve problem by observing some phenomenon from other things. For example, without the observing the birds flying in the sky freely, human cannot come up with the idea of inventing air plane and never design a structure looks like a bird; without observing the fish flying in the ocean freely, human cannot come up with the idea of inventing submarine and never design a structure the have the same mechanism of the fish.
Shijia Liu 6:29:16 11/16/2015
Predicting Tie Strength With Social Media: In this paper, at beginning, it shows us what Tie Strength it is. The author take a us to have a look about tie strength and the substantial line of research into its characteristics. Then this paper discuss four researchers' proposals of tie strength. Strong ties are the people you really trust, people whose social circles tightly overlap with your own. Furthermore the four tie strength dimensions is amount of time, intimacy, intensity and reciprocal services. Above on that, the paper told us the variables which included the predicts variables and dependent variables and how they decide their participants. Then through out the analysis about their collective data the paper shows us the relevant result and discussions.===================Design Lessons from the Fastest Q&A Site in the West: The first sentences in this paper, it directly told us the main target of this paper is Stack Overflow,a Question & Answer site for programmers. By presenting some previous work on that the paper showed that how Stack Overflow work and what is the performance of Stack Overflow and it proved by several perspectives and talking points. After that, this paper discussed some qualitative study, it helped the behaviors and make sense that why it is going to work. Furthermore, it shows us some challenge and future directions and considerations about its developing.
Mingda Zhang 8:48:48 11/16/2015
Predicting Tie Strength With Social Media This paper illustrates an idea of using social media data to predict the tie strength, relationships with a linear prediction algorithm. The concept of tie strength has been proposed in social scientific studies for a long time. The idea has been widely accepted that closeness of people differs a lot, from acquaintance to best buddies. In real life, friendship exists continuously, but on the Internet, is is usually treated as binary: being friend, or not. The importance of this paper includes that it extends the concept to the Internet. The authors uses Facebook profiles as data source and builds their models with 74 potential predictor variables. Generally, it could be categorized into 4 dimensions, including time, intimacy, intensity and reciprocal services. The authors tried to extract important factors from Facebook profiles and other characteristics to predict actual closeness of two friends. With user study experiments, the authors were able to crosscheck their predictions with the actual relationship descriptions and qualitative analysis from the subjects. Finally they focused on 15 predictive variables. From this paper, several lessons could be learned and inspired further studies not only in social computing. First, it once again verifies the importance of borrowing concepts from other area and extending to computer science study. In fact this approach has contributed much to both fields. Second, this paper reveals a new possibility way to analyze the social media data. I believe that in the future, with the increasing capability to rapidly analyze massive data in short period of time, researchers could analyze not only Facebook but also all different websites and crosscheck the identity information. Such techniques could be used in applications in Internet security. Design Lessons from the Fastest Q&A Site in the West This paper mainly talks about a most popular Q&A website, Stack Overflow. By comparing SO with other alternative websites, researchers noticed that users of SO usually provides answers with higher quality within short period of time. In this paper they focused on the design of SO and analyzed its advantages, trying to understand why users of SO were performing better than other websites, and hoping to migrate such designs to other websites. According to the authors, three aspects contributed mostly to the success of SO. First, it created an competitive environment. By using votes mechanism, answers are checked by other users and it motivates users to generate high quality answers. Second, it formed a sense of trust within the community. This benefits lead directly to higher answer quality. By linking together with Facebook and other social media profiles, experts in SO could be recognized as experts and credibility increased by such design. Last but not least, if focused on evolving the design. This motivates users to review their answers based on the feedbacks and improves their answers. In this way it helps to create more meaningful answers in the future. From my personal experiences, I do notice that Stack Overflow has many excellent explanations on difficult topics and skills relating computer science and programming. Since most of its contents were contributed by users, it could be treated as crowdsourcing. One interesting thing about this paper is that it differs from traditional technology report. Since the authors are not the developers of Stack Overflow, they focused on the analysis from the perspective of an user.
Mahbaneh Eshaghzadeh Torbati 8:55:17 11/16/2015
Critique for Predicting Tie Strength With Social Media In general, in this paper the authors discussed how to determine the tie strength using social media. It is a great achievement in the research between tie strength and social media. In my idea this paper is valuable. It help us to have the ability to determine tie strength by using social media. Strength of tie includes the amount of time, the emotional intensity, the intimacy, and the reciprocal services. They determined the dimension of tie strength. These dimensions can determine the tie strength. In the paper, the author also did the user study. The result shows that the approach has a very great performance. Determining the tie strength can bring social computing a great improvement. By knowing the relationship between people, researchers, who are working on social computing, can have a great chance to make great social software and give user a great using experience, like find out people who are your friends on the SNS website that are not your friends, or even help you to find people that you may want to talk with. It may connect people more in the society. -----------------------------------------------Critique for Design Lessons from the Fastest Q&A Site in the West. This paper talked about analysis why Stack Overflow has very great success. They also find out how to make website success. In this paper, author mainly analysis why Stack Overflow success. Q&A system performance of this website is extremely high. There are a lot of reasons that lead to this success. The first reason is that there are a lot of user for that website so that questions will have a great chance to be viewed by others. Also the website have a great visibility so that users can easily see the questions and bring the answers. I have a lot of experience of using Stack Overflow. You can easily find the questions that you may want to ask on it so that you can find the answer quickly. But it limited to some common questions. If you always find answer from the website. You always trust the website that they can bring the answers so that when you find that the answer is not in the website, you may want to ask the question on the website. It bring a lot of user to the website, which make the website better.
Kent W. Nixon 9:01:27 11/16/2015
Design Lessons from the Fastest Q&A Site in the West This paper discussed the design decisions behind one of the most popular, and most technical, question and answer sites on the web – Stack Overflow (SO). The paper claimed that SO was unique in that is offered reliably fast response times (10 minutes for first answer, 20 minutes for true answer) to specific, technical questions in a complex field (programming). The authors seek to identify the reason that SO is able to be so successful despite its extremely technical focus and unpaid community. It is discussed how specific design decisions were made in order to encourage question answering as opposed to idle discussion, such as by randomly ordering answer posts irrespective of chronological order given that they all had the same level of "helpfulness." Also, the community guidelines highly favored detailed, specific questions which would result in a single, best answer instead of philosophical question which could be easily debated. Further, the strong technical background maintained by the site creators allowed them to influence well-qualified community members to join the site, as their reputation in the field inspired some level of trust. These members were then encouraged to stay by the site providing a "ranking" or "leveling-up" system which fostered a spirit of healthy competition on the site. This paper is in no way related to may research, or probably anything I will ever do. However, it was interesting to read about how SO came to be, as it is quite an anomaly. I have used the site several times when looking for answers to programming questions, and have been continually surprised by the quality of answers I find there. It was interesting to read about the amount of work that must go into maintaining the site to keep it that way. Predicting Tie Strength With Social Media In this paper, the authors discuss the ability to predict social tie strength between individuals given information harvested from Facebook. This research builds off of previous work in the field, specifically that social ties can be defined as "weak" or "strong," and that each type has its own uses. For example., weak social ties can often help someone who is looking for a new job. The authors examine a number of features they are able to harvest from Facebook, such as wall posts, private messaging, related friends, and etc., and discover some interesting relationships. For example, the length of private message exchanges has a negative correlation with friendship strength, the opposite of what would be expect. The authors eventually arrive on a linear model which is a binary predictor of whether a friendship is strong or weak. This predictor is able to function with 85% accuracy. This is, again, not at all related to my research, but it was interesting to read about what I could assume was one of the first instances of Facebook data mining. This is something that is done automatically and at large scales nowadays, so it was nice to learn about one of the first approaches.