Make Money From Your Blog

Unluckily, a few of these blogs do very little money though they contain lots of excellent educational content. Some of the reasons for this are which they either have very little movement or the owners haven't monetized them. If you wish to earn money with your blog then these two elements are extremely significant. When you get circulation to your website you need to make good use of it to make cash.



Here are a number of the tips about how to earn money from your blog. People have even been known to ask to promote on some blogs. If you write useful posts and individuals read them then you need to also make these people your visitors. Add a contribution box to each page of your blog. Ponder writing and distributing articles to article reference books on your blog as well as the merchandises you suggest in order that you could get paid a fee when an individual purchases what you're offering on your web site. When you've a following on your web site, for instance, through RSS feeds then use your web site to often speak to these customers plus other new readers. The more people get acquainted with about you, your business as well as what you provide, the more possible they're to refer other folks to your blog. Often Update Your Blog need to give the individuals who read your blog grounds to return so what better means to do it than to frequently update it with new and enlightening content.

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Make Money From Facebook

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Mero anht msethnoi,g ot ekam this cacnhe a gwreirnad one yluo'l vhae ot estadiver yoru ekoaoFcb apge dan egt sa elvsear nsfa as yuo acn. If you look at individuals who earn money with Twitter, their largest concern is to add as several followers as they could. Clearly, the more fans and buddies you've, the more money you'll ultimately make with these applications. It's possible for you to sell something on your FB page using this identifying app. 

lCr,lyae tath nsame even whne you do ssoessp ayn tmie elvaaa,lbi uyo aym hsow otrhe poeepl meit no uroy pgae dna enar emnoy whti het fee you egt orf nislegl tehri tffsu. earsPfseC stel ouy elriayd ells nay ehradnicsem fmro aint rckaBa aaOmb to rop krBaca baOma htSrsi adn ernehtvyig seel no yrou eapg nad aenr mye,no wrhethe it si meda by yuo ro rhetaon noespr. 4- Eethr si an tretnnie eist hewre uyo oulcd rane hacs nainregws noeusistq kdaes yb erefdiftn peleop. You hlosdu ues heirt wne oicapitalnp on Fceaoobk ot rane emyno ewhil vggiin eidacv ot disildanuiv evro hte teonehepl. fI uyo era good ta iiggvn vceia,d ihts nac be hte way fro uoy ot maek omes ditdoaailn cash no ckoFboae.

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How Social Media Can Be a Researcher’s Miracle or Downfall

Cassie Phillips is a freelance technology writer who also dabbles in social media. She’s a firm believer that everyone can find a use for social media whether to make friends or conduct a research experiment. Like technology, she finds social media is just another tool to add to one’s arsenal. @securethoughtsc

The idea of combining social media and research at first might be at odds with one another, but they actually complement one another. Research involves the production, use and consumption of knowledge. Before social, scientists and researchers disseminated information via conferences, journals, peer reviews and publication. What brings all of these events together is collaboration. This is where the true benefits of social become apparent.

Finding Information

You probably already have a system in place to find journals and articles that will suit your research. This can include using information portals, attending meetings and even focusing on certain peer-reviewed journals. While still useful, this takes time and can also lead to information overload. Social media can help you find more relevant information and sift through the noise. Following researchers within your discipline can help you find articles and journals that may be particularly valuable to you.

On the flip side, you still need to verify the sources you find online. Anyone can publish an article or post on the internet, so it's more important than ever to check sources and make sure what you're reading is legitimate.

Knowledge Creation

Most researchers view data generation as the main aspect of the job. For the most part, this means finding other literature that supports your research. However, the other important aspect is ensuring you publish and disseminate the information at the right time. So where does social media fit especially when there are risks in communicating your research while it’s still going on? After all, it can reduce your chances of getting published while also providing ammunition against you should you make a mistake. And with social, there’s also the possibility your account might get hacked, especially if your internet connection isn’t secure, though luckily there are ways to protect yourself.

So what are the benefits? Consider this example. Marianne Hatzopoulou, a civil engineer professor, wanted to research the impact of air pollution on cyclists. She turned to Twitter and sent out a couple of tweets encouraging people to fill out a survey. A popular cycling blog found the survey and then wrote an article. This then got picked up by a local newspaper, which then led to coverage on a radio show and a major network.

Spreading the World

Perhaps the most attractive quality of social media is its ability to disseminate information. Above all, social media is about engagement and communication, making it ideal for researchers to reach a wider audience.

Of course, depending on the type of article you produce, you’ll reach a very particular audience. More scholarly and academic articles will likely attract other researchers in your discipline. However, this means you’ll likely alienate the layperson as they won’t want to wade through pages of information.

Since social media is such an effective tool in attracting attention, it might backfire. Always double check before publishing anything on social media to ensure the post doesn’t come off as offensive or in poor taste. Always read a post multiple times before hitting the send or publish button. One poorly worded tweet can reach thousands of people and ultimately lower your credibility not only among your followers but the scientific community as well.

While there are obvious benefits to using social media, it still doesn’t replace face-to-face interactions. If used improperly, it may end up hurting your reputation and research more than it helps. At the end of the day, it all depends on how you approach it and how you engage with your community.

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“The Big Data rich and the Big Data poor”: the new digital divide raises questions about future academic research

Blog originally published on 22nd November 2016 on London School of Economics blog-site  http://blogs.lse.ac.uk/impactofsocialsciences/2016/11/22/the-big-data-rich-and-the-big-data-poor-the-new-digital-divide-raises-questions-about-future-academic-research/ 

Data is being created faster than ever before. However, as Kate Metzler explains, limited access to this big data is creating a digital divide between large companies and the broader scholarly community. To compound this problem, there is also a big data analysis skills gap that further hinders the progress of social science. Without access to these datasets or the expertise to analyse them, research is confronted with a replication crisis and is vulnerable to commercial motivations.

“Data is the new oil.” Clive Humby, mathematician and architect of Tesco’s Clubcard, is credited with saying this first in 2006, and it’s been repeated numerous times in the last decade. The comparison between data and oil refers to its value being extracted through refinement; or in the case of data, through analysis. Unlike oil, data is being created at a faster pace than it can be consumed, or analysed. We’re awash with data. You may have heard it said that “90% of all the data in the world has been generated over the last two years.” Or, as Hal R. Varian, Chief Economist at Google, puts it another way: “A billion hours ago, modern homo sapiens emerged. A billion minutes ago, Christianity began. A billion seconds ago, the IBM PC was released. A billion Google searches ago … was this morning.”

The capacity to collect and analyse massive datasets has already transformed fields such as biology, astronomy, and physics, and for many, the ‘big data revolution’ promises to ask, and help us answer,fundamental questions about individuals and collectives. But who gets access to all this data we’re producing through our increasingly networked and digital lives, and for what purpose?

Image credit: Divided by David Wan. This work is licensed under a CC BY 2.0 license.

In 2012, danah boyd and Kate Crawford offered a provocation that the limited access to big data was creating a new digital divide between “the Big Data rich and the Big Data poor.” It’s only companies, and the social scientists working within these companies, that have access to really large social and transactional datasets. The broader scholarly community usually does not because companies refuse to release it or because purchasing it costs too much.

Recently, I conducted a survey of more than 9,000 social scientists to learn more about researchers who are engaged in research using big data and the challenges they face, as well as the barriers to entry for those looking to do this kind of research in the future. 32 per cent of respondents who are currently engaged in big data research reported that getting access to commercial or proprietary data was a “big problem” for them:

Figure 1: Challenges facing big data researchers (n = 2273)

But it isn’t only the question of who can access data that leads to divides. As boyd and Crawford point out, and our survey supports, there is also a skills gap holding social science back: the level of quantitative and programming skills required for big data research make it a challenge for educators to introduce it into traditional social science degree courses as there is little time or expertise amongst teaching faculty:

Figure 2: Challenges facing educators teaching big data (n = 1212)

Why does it matter?

So who cares if academic social scientists can’t do big data, either because they can’t access the data and/or don’t have the skills they need to engage with it? Why not just have companies like Twitter and Facebook analysing social media data? Some have even gone so as far as to argue that academics should not engage in research that can be done better by industry.

There are a couple of reasons why this is problematic. Firstly, because replication is the engine of science, and irreproducible research slows progress. If only researchers within companies can access and analyse big social datasets, “those without access can neither reproduce nor evaluate the methodological claims of those who have privileged access”.

And secondly, and arguably most importantly, the motivations of industry researchers and social scientists may differ in ways that may really matter. Big data research conducted by companies is usually in service of a single overarching goal: to sell you more stuff. Social scientists with the right skills and access to the right data may use their research to contribute to the body of knowledge, with the aim of better understanding and improving social outcomes.

The questions boyd and Crawford pose at the start of their paper summarize this perfectly. They ask:

“Will large-scale search data help us create better tools, services, and public goods? Or will it usher in a new wave of privacy incursions and invasive marketing? Will data analytics help us understand online communities and political movements? Or will it be used to track protesters and suppress speech? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what ‘research’ means?”

As of yet, the answers to these important questions are unclear.

Read more in the recent SAGE Publishing white paper revealing full results of the survey, “Who is Doing Computational Social Science? Trends in Big Data Research.”

About the author

Katie Metzler is Head of Methods Innovation at SAGE Publishing. Katie is responsible for content strategy and innovation for SAGE’s award winning online platform for researchers, SAGE Research Methods, which includes SAGE Research Methods Cases, SAGE Research Methods Datasets and SAGE Research Methods Video. In addition to heading up the London commissioning team for the SAGE Research Methods platform, she is part of a new team at SAGE whose mission is to improve social science by equipping every researcher with the skills and tools they need to work effectively with big data and new technology. At SAGE, we believe big data and new technology are fundamentally changing how we make sense of the world and that social science needs to play a critical role where this impacts on society.

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Identifying popular tourism attractions in London by using geo-tagged photos from Flickr

Dr. Yeran Sun is a postdoctoral researcher at Urban Big Data Centre, University of Glasgow, UK. His research interests include big data and urban studies, social media research and sentiment analysis, transport and social inequality, transport and public health.

Social media data offers crowd-sourced data to social science research. In particular, GPS enable-devices, such as smart phones, allow social media users to share their real-time locations in social media platforms.

In my presentation, Flickr geo-tagged photos are used to identify popular tour attractions in London.

‘Geo-tagged’ photos and tweets of Flickr, Instagram and Twitter users tell us the footprints and mobility of users. Compared to Instagram and Twitter, Flickr has a large portion of tourists. Geo-tagged photos from Flickr users are used as crowed-sourced data in recent tourism research. However, the population of geo-tagged photos are not proportional to the population of real tourists’ footprints. Therefore, visits to popular tourism attractions such as landmarks are likely to be over-represented by Flickr photos, while visits to unpopular tourism attractions are likely to be under-represented.

Although geo-tagged photos are biased, they could be used to reflect popularities of tourism attractions that have no ticketing records, such as central squares, public statues, public parks, rivers, mountains, bridges and so forth. Crowd-sourced data from Flickr photos can be used to measure popularities of tourism attractions without ticketing records. As clusters of photos tend to take place around popular tour attractions where tourists like to take photos, we could identify popular attractions by detecting significant spatial clusters of geo-tagged photos.

In my presentation, significant clusters are detected by using a density-based clustering method called DBSCAN.  Most of those clusters spatially overlap popular tour attractions in London. In my presentation, free-to-use tools QGIS and R are used to map geo-tagged photos and carry out cluster detection respectively. Additionally, to run the DBSCAN algorithm we need to install a package ‘dbscan’ in R. Via Flickr APIs (https://www.flickr.com/services/api/), we can download public Flickr data including photos, tags and coordinates by defining geographic boundaries or searching for keywords.  There are API kits written in a variety of languages, including C, Delphi, Java, Python, PHP, .NET, Ruby and so on. You might also use shared Flickr data for your research. Yahoo Research share Flickr data with researchers (https://research.yahoo.com). Shared datasets can be found here (https://webscope.sandbox.yahoo.com/catalog.php?datatype=i). 

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Facebook as a Tool for Research

Gill Mooneyis a doctoral researcher, studying at the University of Leeds. Her research interests are currently focused on social class and social media. She completed her undergraduate degree as a mature student in sociology at the University of Hull, and prior to this was employed as a project co-ordinator for a young people’s sexual health charity in Hull. @gillmooney

My research is concerned with the ways in which we know, understand and produce social class in the digital environment of the social networking site (SNS), Facebook. The research will provide valuable insights into how social networking is changing the ways we may relate to one another both online and offline, as well as the effect it might be having on broader understandings of social class.

Facebook is the topic of the research, the site in which parts of it take place, and a tool for facilitating its logistics and practicalities. I am using it for recruitment, communication with research participants, and using content collected from Facebook as stimulus for discussion in focus groups and interviews. This combination of online and offline methods and approaches requires reflexivity to run smoothly, but maintaining a link between online and offline is essential for providing data that represents the relationship between those two spheres in terms of how individuals perceive and produce social class, and the broader effects that may have.

Recruitment

I specified Facebook as the means through which I would recruit participants, partly because I would know that they were definitely likely to be regular Facebook users, and have a reasonable understanding of how the platform functions, but also because I want to keep as much of the research as possible within the psychic environment of Facebook, to help participants stay focused on discussing things that happen there, and keeping the research framed within Facebook. I began by asking members of a general interest Facebook group of which I’m a member to share the call for participants on their own accounts. There are considerable ethical implications in using Facebook in this manner, especially when using my personal account for recruitment. It could result in a pool of participants who are connected to me personally in some direct or indirect way, which has the potential to compromise the integrity of the research or cause tension in my personal relationships. Precautions were put in place to avoid these kinds of conflict, mainly through checking possible connections to potential participants.

Communication

I set up a Facebook account in the name of the research, specifically for the purpose of handling communication and logistics with participants, again as part of wishing to keep all elements of the research within Facebook as far as possible. Participants add the account as a friend, and then I can use the messages tool to stay in touch with participants, arrange focus group and interview sessions, and send them links to consent forms and other information.

This has proven to be an effective means of staying in touch, and it means I can provide information quickly and easily in a medium that is both convenient for the participants and within the environment that I’m researching.

Stimulus for discussion

There was some concern that during the focus group sessions it would be easy for the discussion to deviate away from Facebook, and that it might be difficult to even begin talking about it in a face-to-face encounter, with others. In response I devised a ‘dummy’ Facebook newsfeed page as a way to stimulate discussion, and maintain focus on Facebook. By using this page, I can guide discussion by referring to it and asking the groups to comment on different elements within it, framing my questions around it to stay on track. Class is a difficult topic to discuss, everyone understands it differently and has had different experiences of it, so rather directly addressing it, I am able to talk about self-representation more generally in terms of Facebook and explore how class shows itself there. The content for this dummy page comes from the pages of people in my own friendship network who volunteered, and is subject to a very rigorous consent and anonymisation process.

For the interviews, participants’ own shared content is used. They provide consent for me to select some items they have shared, and then it’s used as a means to stimulate discussion, serving a similar purpose as the dummy page.

Conclusion

Using Facebook as a tool for research requires significant planning and reflexivity throughout the whole research process, but can offer benefits in terms of having access to large networks of individuals for recruitment purposes, as well as an easy and convenient way to stay in touch with participants. The difficulties in planning are related to the considerable ethical implications of using content shared by participants, and ensuring informed consent is in place at all times.

Facebook is a crucial site for research that seeks to understand contemporary society, as its use grows and it becomes further embedded in the lives of its users. Developing well thought out approaches to this kind of work is essential for maximising the research potential of the platform, and for making sure that research is carried out with integrity.

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On Social Media Analytics

Phillip Brooker is a research associate at the University of Bath (UK) working in social media analytics, with a particular interest in the exploration of research methodologies to support the emerging field. Phillip is a member of the Chorus team; a Twitter data collection and visualisation suite (www.chorusanalytics.co.uk). He currently works on CuRAtOR (Challenging online feaR And OtheRing), an interdisciplinary project focusing on how “cultures of fear” are propagated through online “othering”. @Chorus_Team

NSMNSS events have always been good value for me. I haven't quite been a part of the network since it kicked off, but I certainly have tried to be an 'active member' for the years that I have been involved with it. So when Curtis Jessop emailed me to ask if I'd give a talk on the practicalities of using Chorus to do social media analytics research, I jumped on it. Moreso than telling people about our software and what we've used it to do, these events are always the perfect chance to hear about innovative current research in the field. I won't go through my talk in too much detail here since I generally try not to be too reductive about how Chorus might be used in social research. Best to download it, watch the tutorial video, read the manual and then play about with it yourself (all of which you can do at :::PLUG ALERT!::: www.chorusanalytics.co.uk). Suffice to say that my talk aimed to run through the basic features and functions of Chorus as a free tool for collecting and (visually) analysing Twitter data. This included a demonstration of the two different data collection modes – the more familiar query keyword search which you can use to look for hashtags and so on, as well as our native user following data collection function which lets you capture sets of user’s Twitter timelines. And from there, I ran through the different ways of visualising data within Chorus – in brief, the timeline explorer which provides a variety of metrics (e.g. tweet volume, percentage of tweets with URLs, positive and negative sentiment, novelty and homogeneity of topic) as they change across time, and the cluster explorer which produces a topical map of the entire dataset based on the frequency with which co-occur with one another. The aim here was to show how Chorus might be used by researchers to answer lots of different types of research question, both as a full all-in-one package, but also in a more exploratory way if users want to quickly dig into some data for a pilot study or similar – readers especially interested in what Chorus might offer might find one of our recent methods papers useful (available at: http://bds.sagepub.com/content/3/2/2053951716658060).

However, what I want to comment more pointedly on in this blog is the NSMNSS event itself, because to me it marks something of a turning point in social media analytics, where it's finally becoming very clear just how distinctive we've made (and are continuing to make) the field. There seems to have always been this worry that working with digital data runs the risk of turning the social sciences into unthinking automata for blindly spotting patterns – the supposed ‘coming crisis of empirical sociology’ referred to by Savage and Burrows in 2007. And that characterisation has not really disappeared, despite social media analysts natural objections to it as a way of representing our work. Thus far, social media analytics has (arguably) necessarily had to progress in a way that directly references those concerns – researchers have made it their explicit business to show, through both conceptual and empirical studies, that there is more to social media data than correlations. However, at this most recent NSMNSS event I got the sense, very subtly, that something different was happening. As a community, we seem to be moving past that initial (and I reiterate, very necessary!) reaction into a second phase where we’re beginning to be more comfortable in our own skin. We’re now no longer encumbered by the idea of social media analytics as “not data science”, and we’re seeing it recognised more widely as a thing in and of itself. As I say, it might seem a subtle distinction, but to me it suggests that finally we’re finding our feet!

Of course, this doesn’t mean we have neatly concluded any of the long-standing or current arguments about the fundamental precepts of the field – my background in ethnomethodology and ordinary language philosophy gives me a lot to say about the recent incorporation of ideas from Science and Technology Studies into social media analytics, for instance. But nonetheless, for me, this event has demonstrated the positive and progressive moves the field seems to be making as a whole. We already knew it of course, but it’s clearer than ever that there are very interesting times ahead for social media analytics!

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