Social Media on the Road: The Future of Car Based Computing: 50 (Computer Supported Cooperative Work)

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Both else without being followed back. On Face- of us are aware that current social media book, friending someone requires acquies- platforms reach many more people than cence on both sides: the person making the Internet did in the heydays of blogging. On That is not the problem. Neither is it the Twitter, any public account can be followed existence of more frivolous or mundane with just a click, without having to formally content online; cute cat and baby images ask for permission.

These structures are are part of the package. The problem is the formed through decisions made by the peo- shift in the architecture of the Internet. In ple who run, administer, and create the code ways both dramatic and subtle, the shift for these platforms, and are implemented has begun to create new profound and far- by in-house coders, resulting in different reaching problems.

Facebook tends to have smaller net- The power of the Internet comes from our works made up of friends, family, and ac- relationships on it. Online about what is relevant, important, and vis- platforms are shaped not only by the code ible; and seek to shape our experiences for that structures visibility and access, but by commercial or political gain.

This combi- How did we get here? And how much nation gives online platforms powers for power is now concentrated in these plat- which there are no simple analogies in the forms? The answers to these questions are offline world. Everyday objects are in- play a major role in shaping society. Some of how online platforms work, to the role ar- these data are accessed by governments for chitecture plays offline. Fi- tractable via direct questions, and far exceed Zeynep nancial institutions mine data to check the scope of information that could be gath- Tufekci credit-worthiness.

Occasionally, the data are ered about a voter via traditional methods. Ordinary people have very lit- process of sifting through many varieties of tle idea about who holds what kind of data data, with the proviso that the data are deep about them, or how the data are used. The and rich enough. Inferring political vari- amount of accumulated data and the asym- ables about a person does not require their metry of power between the people who participation in overtly political websites or are monitored and surveilled and the plat- conversations.

The collection of about surveillance. In a networked ethnicity, religious and political views, per- society, computation brings another dimen- sonality traits, intelligence, happiness, use sion of asymmetric power. Through tech- of addictive substances, parental separation, niques that can be loosely collected under age, and gender. By using only their social have gathered these data can infer from media imprints again, not directly asking them information that has never even been questions of individuals , researchers have disclosed. It does not take much of precision, and crucially, often without ask- imagination to see that advertisers will ing the voter a single direct question.

However, if we Pirates times. This could mean using systems that or whatever type of cases they are present- discriminate only against women who are ed with into various categories. Machine- statistically likely to become pregnant soon. Social media platforms in- all employees in the database.

Without re- creasingly hold the kind of data that can be ceiving direct instruction or a recipe about used in these ways. On the surface, daunting issues. In fact, if platforms. In reality, though, all that a man- sonal in nature to news articles. Increasing- ager might know is that the system places ly, for many population segments ranging potential hires into one category or the oth- from younger people in developed coun- er, without any understanding of what parts tries to populations just coming online in of the social media big data set were used poorer countries, Facebook has become the as signals for a particular outcome.

For ex- ingly the norm, a user will simply launch ample, Google rankings are hugely conse- the Facebook app and rarely encounter the quential. A politician can be greatly helped open Web at all. A recent study sions such power, and invisibly so. In one showed that slight changes to search rank- study, The news gust What started as a community feed is a world with its own laws of phys- shaken over the police killing of a young ics, and the deities that rule it are Facebook man under murky circumstances grew into programmers. In this world, some types of major protests after the police responded information are nudged and helped to to initial small-scale—and completely non- spread more, while others are discouraged.

A few national journalists, as well as enced by platform design and code. The burgeoning unrest and conflict es. About three million lected, saw a slightly more social version of tweets were sent before the mass media be- the encouragement, noting also which of gan covering events in Ferguson. Pirates ter hashtag. Why are they dictating who sees what? Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery.

Recent counts indicate that there are more than newsgroups, with an average traffic of more than MB per day [1]. The newsgroups carry announcements, questions, and discussions. In a discussion, often called a thread, one article induces replies from several others, each of which may also induce replies. The January 24, estimates of netnews participation indicate that more than , people posted articles in the previous two weeks.

There are many more "lurkers" who read but do not post articles. Clearly, a lot of people are getting value from these bulletin boards. In fact, netnews' rapid broadcast nature and widespread readership has reshaped the way the computing community works. System administrators depend on netnews to keep in touch with the latest development work, the latest security holes, and the latest bug fixes.

Researchers depend on netnews as a way of keeping up-to-date on new research directions and important results in between conferences. Many others use netnews just to keep in touch with other people around the world, to learn about new books, new recipes, new music, and what life in other cities is like. Over the years netnews has become a principal medium for sharing among computer users. Even so, the experience of using netnews is not completely satisfying. Almost everyone complains that the signal to noise ratio is too low.

Writers cannot easily tell whether their comments are valued, except by the vocal few who post responses. Some seem not to care about reader interest, only about their own right to write. Moreover, tastes differ, so that no one article will appeal to all the readers of a newsgroup. Each reader ends up sifting through many news articles to find a few valuable ones. Often, readers find the process too frustrating and stop reading netnews altogether. Netnews provides two mechanisms that help readers limit their attention to articles likely to interest them. First, the division of the bulletin board into newsgroups allows readers to focus on a few topics.

When the number of postings in a newsgroup gets too large, it is often split into two or more newsgroups with identifiable subtopics. Second, some newsgroups are moderated. Attempted postings to these newsgroups are automatically forwarded to the moderator, who decides whether or not they belong in the newsgroup. Usenet propagates only those articles that receive the moderator's stamp of approval.

In addition, software packages for reading netnews hereafter referred to as news clients provide other mechanisms that ease readers' burdens. First, most news clients display a summary of the author and subject line for each message in a newsgroup. The user then indicates which articles she would like to read. Second, most news clients display all of the articles in a particular discussion thread together. Some initially show only the first article in each thread, allowing users to quickly peruse the current discussion topics. Third, some news clients provide "kill files. If a user puts the subject line of an article into the kill file, no further articles on that subject will be displayed.

If a user puts the author's name into a kill file, no further articles from that author will be displayed. Finally, some news readers provide string search facilities. If the user is particularly interested in articles that mention "collaborative filtering," the news client can find them. GroupLens provides a new mechanism to help focus attention on interesting articles.

It draws on a deceptively simple idea: people who agreed in their subjective evaluation of past articles are likely to agree again in the future. After reading articles, users assign them numeric ratings. GroupLens uses the ratings in two ways. First, it correlates the ratings in order to determine which users' ratings are most similar to each other. Second, it predicts how well users will like new articles, based on ratings from similar users.

How To Sell More Cars Using Social Media - Social Media For Auto Dealers and Car Sales

The heart of GroupLens is an open architecture that includes news clients for entry of ratings and display of predictions, and rating servers for distribution of ratings and delivery of predictions. The general problems of information overload and low signal to noise ratio have received considerable attention in the research literature.

We use the term information filtering generically to refer both to finding desired information filtering in and eliminating that which is undesirable filtering out , but related work also appears under the labels of information retrieval and selective dissemination of information [2]. In addition, research on agents [12, 13], user modeling [1, 9], knowbots [8], and mediators [21] has explored semi-autonomous computer programs that perform information filtering on behalf of a user.

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Malone et al. The three categories provide a useful road map to the literature. Cognitive, or content-based filtering techniques select documents based on the text in them. For example, the kill files and string search features provided by news clients perform content filtering.

Even the division of netnews into newsgroups is a primitive example, since a reader restricts his attention to those articles with a particular text string in their "newsgroup:" field. Other content-based filtering techniques could potentially be used as well. The profile of which texts to include or kill could be more complex than a collection of character strings.

Alternatively, the profile could consist of weight vectors, with the weights expressing the relative importance of each of a set of terms [4, 5, 16]. Some content filtering techniques update the profiles automatically based on feedback about whether the user likes the articles that the current profile selects. Information retrieval research refers to this process as relevance feedback [17]. The techniques for updating can draw on Bayesian probability [2], genetic algorithms [18], or other machine learning techniques.

Social filtering techniques select articles based on relationships between people and on their subjective judgments. Placing an author's name in a kill file is a crude example. More sophisticated techniques might also filter out articles from people who previously co-authored papers with the objectionable person. Collaborative filtering, based on the subjective evaluations of other readers, is an even more promising form of social filtering. Human readers do not share computers' difficulties with synonymy, polysemy, and context when judging the relevance of text.

Moreover, people can judge texts on other dimensions such as quality, authoritativeness, or respectfulness. A moderated newsgroup employs a primitive form of collaborative filtering, choosing articles for all potential readers based on evaluations by a single person, the moderator. The Tapestry system [6] makes more sophisticated use of subjective evaluations.

Though it was not designed to work specifically with netnews, it allows filtering of all incoming information streams, including netnews. Many people can post evaluations, not just a single moderator, and readers can choose which evaluators to pay attention to. Moreover, filters can combine content-based criteria and subjective evaluations. For example, a reader could request articles containing the word "CSCW" that Joe has evaluated and where the evaluation contains the word, "excellent".

Our work is similar in spirit to Tapestry but extends it in two ways. First, Tapestry is a monolithic system designed to share evaluations within a single site. We share ratings between sites and our architecture is open to the creation of new news clients and rating servers that would use the evaluations in different ways.

Second, Tapestry does not include any aggregate queries. The rating servers we have implemented aggregate ratings from several evaluators, based on correlation of their past ratings. A reader need not know in advance whose evaluations to use and in fact need not even know whose evaluations are actually used. In GroupLens, ratings entered under a pseudonym are just as useful as those that are signed. Maltz has developed a system that aggregates all ratings of each netnews article, determining a single score for each [14].

By contrast, GroupLens customizes score prediction to each user, thus accommodating differing interests and tastes. In return for its reduced functionality, Maltz's scheme scales better than ours, because rating servers can exchange summaries of several users' ratings of an article, rather than individual ratings. The subjective evaluations used in collaborative filtering may be implicit rather than explicit.

Read Wear and Edit Wear [7] guide users based on other users' interactions with an artifact. The GroupLens news clients monitor how long users spend reading each article but our rating servers do not yet use that information when predicting scores. Economic filtering techniques select articles based on the costs and benefits of producing and reading them. For example, Malone argues that mass mailings have a low production cost per addressee and should therefore be given lower priority. Applying this idea to netnews, a news client might filter out articles that had been cross-posted to several newsgroups.

More radical schemes could provide payments in real money or reputation points to readers to consider articles and payments to producers based on how much the readers liked the articles. Stodolsky has proposed a scheme that combines social and economic filtering techniques [19]. He proposes on-line publications where the publication decision ultimately rests with the author.

During a preliminary publication period, other readers may post ratings of the article. The author may then withdraw the article, to avoid the cost to his reputation of publishing an article that is disliked. GroupLens is a distributed system for gathering, disseminating, and using ratings from some users to predict other users' interest in articles. It includes news reading clients for both Macintosh and Unix computers, as well as "Better Bit Bureaus," servers that gather ratings and make predictions. Both the overall architecture and particular components have evolved through iterative design and pilot testing to meet the following goals:.

Openness : There are currently dozens of news clients in common use, each with a strong following among its user community. Any or all of these clients can be adapted to participate in GroupLens. GroupLens also allows for the creation of alternative Better Bit Bureaus that use ratings in different ways to predict user interest in news articles.

Ease of Use : Ratings are easy to form and communicate, and predictions are easy to recognize and interpret. This minimizes the additional burden that collaborative filtering places on users. Compatibility : The architecture is compatible with existing news mechanisms. Compatibility reduces user overhead in taking advantage of the new tool, and simplifies its introduction into netnews. Scalability : As the number of users grows, the quality of predictions should improve and the speed not deteriorate.

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One potential limit to growth will be transport and storage of the ratings, if GroupLens grows very large. Privacy: Some users would prefer not to have others know what kinds of articles they read and what kinds they like. The Better Bit Bureaus in GroupLens can make effective use of ratings even if they are provided under a pseudonym. Typically a site will declare a machine to act as its news server. Users at each site invoke news clients on their computers and connect to the news server in order to retrieve news articles.

Users can also write new articles and post them to the news server through their news clients. When a user posts an article, it travels from the news client where the article is composed to the local news server and from there to news servers at nearby sites.

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  • After leaving the originating site, an article propagates throughout Usenet, hopping from site to site. Since there is no centralized coordination of the distribution process, an article may arrive at a site via more than one route. Because articles have globally unique identifiers, however, and are never altered once they are posted, any site can recognize a duplicate copy of an article and avoid passing it on. Lotus Notes uses a similar distribution process [10]. The netnews architecture is summarized in Figure 1.

    GroupLens adds one new type of entity to the netnews architecture, Better Bit Bureaus, as shown in Figure 2. The Better Bit Bureaus provide scores that predict how much the user will like articles, and gather ratings from news clients after the user reads the articles. The Better Bit Bureaus also use special newsgroups to share ratings with each other, to allow collaborative filtering among users at different sites. The remainder of this section traces the processes of rating creation, distribution, and use and describes how they meet.

    Figure 1: The netnews architecture. News articles hop from news server to news server. A news client connects to the news server at its site and presents articles to users. Figure 2: The GroupLens architecture. Better Bit Bureaus collect ratings from clients, communicate them by way of news servers, and use them to generate numeric score predictions that they send to clients. Clients connect to a local news server, and can connect to a Better Bit Bureau that uses the same or a different news server. In GroupLens, a rating is a number from 1 to 5, optionally supplemented by the number of seconds which the user spent reading the article.

    Users are encouraged to assign ratings based on how much they liked the article, with 5 highest and 1 lowest. The user chooses a pseudonym to associate with her ratings that may be different from the name she uses for posting news articles. This preserves the ability to detect that two ratings came from the same person, while preventing detection of exactly who that person is. The GroupLens choice of the form and meaning of ratings is only one possibility in a rich design space.

    There are many possible dimensions along which to rate articles: interest in subject, quality of writing, authoritativeness of the author, etc. Rather than a single composite rating, separate ratings on several dimensions could be solicited from readers. Free text ratings could be entered rather than numbers. Readers could be asked to predict how well they think other readers will like an article rather than report how much they themselves liked it.

    Ratings could be restricted only to positive, or only to negative evaluations. The degree of privacy could also be varied, from completely anonymous to authenticated signatures. In fact, an earlier implementation of a Macintosh news client [20] employed ratings with quite a different form than the current GroupLens architecture. Users entered only endorsements, positive ratings, on the assumption that since the signal to noise ratio in netnews is so low it is only important to point out the good articles.

    Readers endorsed articles that they thought others in a known small group would like. Finally, readers signed endorsements with their real names, allowing other people to select all the articles endorsed by a particular friend. A pilot test of that earlier endorsement mechanism at a Schlumberger research lab indicated that a group of seven people may not be large enough to get the full available benefit of collaborative filtering. As we contemplated a much larger group size, we believed that some users would be less willing to sign their ratings and that it would become increasingly difficult for users to know what articles others in the group would like.

    The pilot test also reinforced the importance of making it as easy as possible to enter endorsements. To make an endorsement, a user had to select from a pull-down menu, wait for a window to open up, optionally enter text in the window, and then close it. While the whole process took only a matter of seconds if the user entered no text, it was still significantly longer than it normally takes to go on to the next article.

    Additionally, because the model that used image object data achieved the highest performance, we used that model to assess the feature importance. Fig 6 random forest and Fig 7 logistic regression present the top 15 most important features based on coefficients from the random forest and logistic regression models, respectively. These features were the top 15 average features from the models generated from 5-fold cross-validation.

    Overall, the analysis of feature importance demonstrated clear differences in image content between the age and gender groups. In the random forest model Fig 6 , human-associated objects e. For gender classification, two specific words were the most influential—man and woman. When we compare these results with the F1 scores in Table 3 , the lower performance for gender classification compared with age may be the result of having only a few strong features. The bars represent the feature importance of the forest, along with its inter-tree variability.

    For gender, only two words woman and man strongly affect the model, though the other words still have relatively high coefficients. For the logistic regression model Fig 7 , the feature importance for each user group can be directly compared based on the coefficients. For age, the strongest features for teens included fashion and beauty e. Although the other features seem less influential, their coefficients were still comparable to those for age.

    Features associated with food e. Although there have been many studies that have built prediction models to identify gender and age, our models are unique in that we only used TF-IDF and word embedding for image objects and tags. To further evaluate our models, we compared their performance with that of human evaluators. This also allowed us to determine why humans make mistakes or have less confidence in their decisions. We noticed that many users create an online account name that incorporates their year of birth e.

    After data collection, we manually determined if users were teen or adult, or male or female, by visiting their Instagram page and examining the images they posted. Additionally, we confirmed that the users were not included as part of our previous data presented in Table 1. As a result, we collected new users consisting of teens and adults, males and females to use as test data. We also collected all of the images and tags associated with these users. The data for each user was organized in five different ways: 1 image objects only, 2 tags only, 3 image objects and tags together, 4 real images only i.

    The image objects were represented as text in this stage of the study. For each of these cases, we compared the performance of our models with that of human evaluators, with our models utilizing image objects, tags, and image objects and tags together. This approach also allowed us to use human evaluations as a baseline for the performance of our models. Our survey consisted of three main phases. First, we introduced our study and detailed the process for the participants who were to act as evaluators.

    Second, we asked the participants to indicate their age, gender, and length and frequency of their Instagram use. Other than the image objects and tags, no additional identifiable user information was presented in the survey. A single survey presented one of these information types for 50 different users i. Participants were asked to determine the age and gender of the user based on the presented information. Based on the test users, we created four surveys of 50 users each, with the users assigned randomly to each survey.

    Each of these surveys had five versions based on the different types of information described above, giving a total of 20 different surveys. Because many studies have demonstrated the reliability and validity of Amazon Mechanical Turk [ 46 ], we used this service to collect responses.

    We obtained informed consent from the participants at the beginning of the survey. For each survey, we recruited 20 participants, thus our 20 surveys were completed by participants in total. Each participant evaluated the age and gender of 50 users, thus we collected 20, responses. All of the responses were complete. When analyzing the results of the surveys, we chose the most common answer for each test user and compared this to the ground truth in order to calculate the accuracy of human evaluation.

    For model evaluation, we converted the image objects and tags of the test users into the same vector space TF-IDF and Word2Vec as for our training data. We then ran our models using the converted vectors and obtained accuracy measurements for the new test sample with our original dataset.

    Finally, we compared the accuracy of our models with those of the human evaluators Fig 9.

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    TF-IDF corresponds to the importance of words. Word embedding corresponds to the meaning and relationships of the words. Fig 10 summarizes the age and gender identification performance of our models and the human evaluators for different types of information. When only image objects were provided, our models exhibited significantly higher performance than the human evaluators for age 0.

    This indicates that image objects appear to contain sufficient information for models to be able to accurately classify users, while human evaluators found these objects more difficult to interpret. They had an average confidence rating of 3. When only the tags were provided, our models displayed greater performance in detecting age than human evaluators 0. Similar to the results from image objects, the levels of confidence for the tag data age: 3. More importantly, when we consider the models only, the model performance using tags was lower than that using image objects for both age 0.

    This indicates that image objects provide more distinctive information for age and gender classification. When both image objects and tags were provided, classification performance did not change considerably when compared to that using image objects only for either the model or the human evaluators. However, when real images were provided, significant differences appeared Fig 10 , blue bars.

    When we compared our models based on image objects with human evaluators using real images, our models still displayed significantly better performance for age group classification 0. However, human evaluators exhibited slightly better performance than the models for gender classification 0.


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    Given that some images used in the study contained faces or were selfies with which we initially expected that human evaluators would perform better , this result highlights the challenge of determining age for human evaluators even when faces are included in the image. The difference in accuracy in the age and gender classification by human evaluators was reflected in their level of confidence in their choices.

    They had a lower average confidence rating when predicting age 3. Lastly, when both a real image and tags were provided, the performance of the human evaluators was slightly worse than that when using an image only age: 0. It seems that including both types of information did not aid the human evaluators in making more accurate predictions. The confidence levels were similar to those with images only age: 3. In summary, these results demonstrate 1 the robustness of our models with a completely new set of test data, 2 the overall better performance by our models compared to human evaluators, and 3 the greater influence of images than tags in determining user characteristics.

    Additionally, the results for the human evaluators reflect the difficulty in assigning age and gender to a user, with many evaluators making incorrect decisions. This suggests that a meticulous study design is required when using human-generated annotations to label data because human classification can vary considerably depending on the data type and study conditions.

    GroupLens: An Open Architecture for Collaborative Filtering of Netnews

    Data that represents human engagement on social media provides ample opportunities to understand how humans behave online. Based on this, we aimed not only to understand the behavioral patterns of user groups through a comparative analysis of large datasets of image objects and tags but also to create prediction models that classified users into teens or adults based on features derived from image objects and tags. We presented an in-depth comparative analysis of user image-sharing practices using deep learning techniques.

    It was found that image objects and tags reflected different types of topic between the user groups. The differences in topic choices between teens and adults, and between males and females, were noticeable. Based on image objects, teens showed significantly more interest in topics related to humans i.

    This could be related to selfie-posting behavior, which is also more frequent for females. This study found evidence of females using hashtags as a form of self-representation. This agrees with prior research, which has reported that the frequent posting of selfies by teenage girls is greatly influenced by idealized beauty standards and peer portrayals of beauty standards [ 47 , 48 ].

    Similarly, other studies have found that females tend to be more likely to use social networking sites to compare themselves to others and to search for information [ 49 ]. Nature was another major topic that distinguished teens and adults. When we examined tag-based topics, interest in nature and location was significantly higher among adults than among teens.

    This suggests that a high proportion of the images uploaded by adults include their surroundings even when the images include humans , whereas most images posted by teens depict humans, faces, or actions. This reflects common gender stereotypes and indicates that the gender differences inherent in many behaviors are mirrored in online spaces [ 49 ].

    It is clear that teens attempt to gain followers and receive more likes by making their activities and images more visible to others, which reflects teen commenting behaviors in response to random users with whom they do not share a social connection [ 50 , 51 ]. These motivations do not seem to be as pervasive for either images or tagging activities for adults. The behavioral characteristics of social media use, which are based on user attributes, allowed us to build a classification model that achieved good performance. Given that our models were based only on the TF-IDF and word embedding of image objects and tags, while most prior models used a more varied range of features, we believe that our model produces an acceptable classification performance.

    It was also found that feature importance differed significantly between user groups. Because images are always available on image-based social media, user classification models that utilize features from images e. For a given image, we had O image objects describing the image, and users themselves self-selected T tags that they felt were related to the image. Despite the acceptable performance of the deep learning model in this study, when the machine extracts O image objects, it can be assumed that there is some information loss.

    However, our study found that image objects were more useful for discriminating age and gender than tags. This raises an interesting question: Does O is higher than T in size on average, thus do tags provide more information? The classifiers that employed images produced a higher classification accuracy than those using tags. The distribution of the topic modeling results also supports this. When we look at the results in Figs 3 and 4 , the image-based topic modeling was more focused on fewer topics, while tag-based topic modeling exhibited a distribution with greater variance.

    This reduced the performance of the models. We can therefore conclude that even though more tags were associated with the images, many of the tags had less influence on the accuracy of user classification than did the images. Another interesting finding was that human raters were less accurate than the models in classifying users by age and gender.

    The lack of a profile photo or bio may have made the prediction task more difficult for the human raters and led them to perform worse than the models. Many prior studies on social media have assumed that tags are representative of their corresponding content including images [ 18 , 19 ]. This is indeed a valid assumption. However, our study demonstrates that image objects have significantly more influence than tags on the classification of age and gender.

    Social networking sites can use the insights from our study to identify user groups that have a strong similarity. The names of image objects and tags can also be used to match users with similar interests while also providing age and gender information. This approach could be used to create an interactive social space that extends beyond simply tracking followers and locations. In addition, because we found that images are more influential than tags in user attribute classification, social media platforms could create keyword suggestion tools via deep learning using images rather than relying on text-based information.

    We believe these functions can provide users with more specifically tailored suggestions of who to follow and what to see. They can also be applied to commercial services, such as product recommendations. One concern for recommendation services is privacy. Therefore, privacy-preserving design decisions for implementing these features, such as allowing users to control their visibility, should be considered. Additionally, researchers have actively been investigating the characterization of users who may suffer from mental illness e.

    Models based on our approach can detect users with certain user attributes e. As discussed earlier, one approach to handling potential privacy issues is to give full control to the user with respect to recommendations some users may find recommendation results useful, some may not. Although our work yielded a number of interesting insights, there are some limitations that need to be addressed in the future. First, our results may not be generalizable because the original user sample used for classification directly provided their age information to some extent either on their profile or through tags.

    In addition, users in their 20s were not considered, which limits the conclusions that can be drawn about age. The next step is to build more comprehensive classification models and to identify users and collect data that represent the diverse range of users on social media. Using information from a greater number of users is also necessary. Second, a more comprehensive analysis can be achieved by combining the features from image and text processing e.

    This analysis could include a study of temporal changes in image-sharing activities and the identification of image objects that potentially lead to privacy leaks, among other topics. While it would be challenging to identify these users, they may have some effect on future findings and insights. Another important consideration for future research is to examine the number of features required for both machine-learning models and humans to produce an acceptable classification performance and how the number of features available affects that accuracy.

    This will allow us to examine whether accuracy improves more rapidly for models or humans and to determine the threshold at which adding an additional feature greatly improves accuracy. By doing so, we can understand how to better utilize the human perspective in building more useful and more accurate models. We demonstrated the strong influence of age and gender on the way people use Instagram based on an analysis of topical and contextual differences.

    Our study highlights that future research should exploit the information contained in images to a greater degree because it can be used to develop more accurate classification models, especially in the absence of accompanying text. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Introduction Social media has penetrated daily life, allowing users to access, create, and interact with a wide range of information.

    Our results support the following conclusions: Content Topics from images and tags are distinctive enough to distinguish age and gender groups. The variance in topic distribution from tags is higher than that from images. Teens and females tend to have a higher ratio of selfies than adults and males. Prediction Models with images outperform those with tags. Models with images and tags combined do not exhibit better performance.

    Models with features from TF-IDF generally perform better than those with features from word embedding. Human versus Machine Performance Our models demonstrate their robustness against new test data by comparing them with human evaluators. Our models yield similar degrees of accuracy for age and gender classification.

    Related work Comparative analyses of age and gender Many studies have used variations in linguistic characteristics to identify gender differences in social media. Modeling user age and gender Predicting and characterizing user attributes such as age and gender has become an active research topic in NLP. Research on instagram Instagram is one of the most popular social networking sites and has high levels of user engagement.

    Research goals Our literature review indicates that many studies have identified and modeled user attributes on social media. Study design Our study consisted of four distinct steps: data collection, data cleaning, feature extraction, and analysis and modeling Fig 1. Download: PPT. Data collection Data collection was conducted in March Data labeling and verification Obtaining accurate data prior to analysis is a critical step. Fig 2.

    Social Media on the Road: The Future of Car Based Computing: 50 (Computer Supported Cooperative Work) Social Media on the Road: The Future of Car Based Computing: 50 (Computer Supported Cooperative Work)
    Social Media on the Road: The Future of Car Based Computing: 50 (Computer Supported Cooperative Work) Social Media on the Road: The Future of Car Based Computing: 50 (Computer Supported Cooperative Work)
    Social Media on the Road: The Future of Car Based Computing: 50 (Computer Supported Cooperative Work) Social Media on the Road: The Future of Car Based Computing: 50 (Computer Supported Cooperative Work)
    Social Media on the Road: The Future of Car Based Computing: 50 (Computer Supported Cooperative Work) Social Media on the Road: The Future of Car Based Computing: 50 (Computer Supported Cooperative Work)
    Social Media on the Road: The Future of Car Based Computing: 50 (Computer Supported Cooperative Work) Social Media on the Road: The Future of Car Based Computing: 50 (Computer Supported Cooperative Work)
    Social Media on the Road: The Future of Car Based Computing: 50 (Computer Supported Cooperative Work) Social Media on the Road: The Future of Car Based Computing: 50 (Computer Supported Cooperative Work)
    Social Media on the Road: The Future of Car Based Computing: 50 (Computer Supported Cooperative Work) Social Media on the Road: The Future of Car Based Computing: 50 (Computer Supported Cooperative Work)

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