A STEP-BY-STEP GUIDE to TWITTER CHATS for Minority Serving Institutions
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How to Find the Best Hashtags for Your Business Hashtags Are a Simple Way to Boost Your Traffic and Target Specific Online Communities
CHECKLIST How to find the best hashtags for your business Hashtags are a simple way to boost your traffic and target specific online communities. This checklist will show you everything you need to know— from the best research tools to tactics for each social media network. What is a hashtag? A hashtag is keyword or phrase (without spaces) that contains the # symbol. Marketers tend to use hashtags to either join a conversation around a particular topic (such as #veganhealthchat) or create a branded community (such as Herschel’s #WellTravelled). HOW TO FIND THE BEST HASHTAGS FOR YOUR BUSINESS 1 WAYS TO USE 3 HASHTAGS 1. Find a specific audience Need to reach lawyers interested in tech? Or music lovers chatting about their favorite stereo gear? Hashtags are a simple way to find and reach niche audiences. 2. Ride a trend From discovering soon-to-be viral videos to inspiring social movements, hashtags can quickly connect your brand to new customers. Use hashtags to discover trending cultural moments. 3. Track results It’s easy to monitor hashtags across multiple social channels. From live events to new brand campaigns, hashtags both boost engagement and simplify your reporting. HOW TO FIND THE BEST HASHTAGS FOR YOUR BUSINESS 2 HOW HASHTAGS WORK ON EACH SOCIAL NETWORK Twitter Hashtags are an essential way to categorize content on Twitter. Users will often follow and discover new brands via hashtags. Try to limit to two or three. Instagram Hashtags are used to build communities and help users find topics they care about. For example, the popular NYC designer Jessica Walsh hosts a weekly Q&A session tagged #jessicasamamondays. -
What Is Gab? a Bastion of Free Speech Or an Alt-Right Echo Chamber?
What is Gab? A Bastion of Free Speech or an Alt-Right Echo Chamber? Savvas Zannettou Barry Bradlyn Emiliano De Cristofaro Cyprus University of Technology Princeton Center for Theoretical Science University College London [email protected] [email protected] [email protected] Haewoon Kwak Michael Sirivianos Gianluca Stringhini Qatar Computing Research Institute Cyprus University of Technology University College London & Hamad Bin Khalifa University [email protected] [email protected] [email protected] Jeremy Blackburn University of Alabama at Birmingham [email protected] ABSTRACT ACM Reference Format: Over the past few years, a number of new “fringe” communities, Savvas Zannettou, Barry Bradlyn, Emiliano De Cristofaro, Haewoon Kwak, like 4chan or certain subreddits, have gained traction on the Web Michael Sirivianos, Gianluca Stringhini, and Jeremy Blackburn. 2018. What is Gab? A Bastion of Free Speech or an Alt-Right Echo Chamber?. In WWW at a rapid pace. However, more often than not, little is known about ’18 Companion: The 2018 Web Conference Companion, April 23–27, 2018, Lyon, how they evolve or what kind of activities they attract, despite France. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3184558. recent research has shown that they influence how false informa- 3191531 tion reaches mainstream communities. This motivates the need to monitor these communities and analyze their impact on the Web’s information ecosystem. 1 INTRODUCTION In August 2016, a new social network called Gab was created The Web’s information ecosystem is composed of multiple com- as an alternative to Twitter. -
Influencer Video Advertising in Tiktok
MIT INITIATIVE ON THE DIGITAL ECONOMY 2021, VOL.4 INFLUENCER VIDEO ADVERTISING IN TIKTOK By Jeremy Yang, Juanjuan Zhang, Yuhan Zhang IN THIS BRIEF ik Tok is among the major online platforms blurring the line Tbetween content and commerce. Some creators of TikTok Influencer videos on the TikTok online videos, known as influencers, simultaneously entertain their platform have emerged as a major, multibillion- audiences and sell them products. dollar force in marketing. We explore what differentiates influencer videos that drive many Our research team studied the difference between TikTok sales from those that drive only a few. influencer video ads that drive many sales and ones that drive fewer sales. We also studied whether it’s possible to predict We develop an algorithm that can be used to which TikTok influencer video ads will drive more or fewer sales. predict the sales lift of TikTok influencer videos. This algorithm uses a Convolutional Neural To answer these and other related questions, we developed an Network to quantify the extent to which the algorithm that predicts TikTok influence video sales lift using a product is advertised in the most engaging new metric that we call motion score, or m-score, for short. This parts of the video, creating what we call a statistic is based on an algorithm that quantifies the extent to motion score, or m-score for short. which the product is advertised in the most engaging parts of the video. Videos with higher m-scores lift more sales, especially for products that are bought on We conclude that a one standard deviation increase in a TikTok impulse, are hedonic, or inexpensive. -
Building Relatedness Through Hashtags: Social
BUILDING RELATEDNESS THROUGH HASHTAGS: SOCIAL INFLUENCE AND MOTIVATION WITHIN SOCIAL MEDIA-BASED ONLINE DISCUSSION FORUMS by LUCAS JOHN JENSEN (Under the Direction of Lloyd Rieber) ABSTRACT With the rise of online education, instructors are searching for ways to motivate students to engage in meaningful discussions with one another online and build a sense of community in the digital classroom. This study explores how student motivation is affected when social media tools are used as a substitute for traditional online discussion forums hosted in Learning Management Systems. The main research question for this study was as follows: What factors influence student motivation in a hashtag-based discussion forum? To investigate this question, the following subquestions guided the research: a. How do participants engage in the hashtag discussion assignment? b. What motivational and social influence factors affect participants' activity when they post to the Twitter hashtag? c. How do previous experience with and attitudes toward social media and online discussion forums affect participant motivation in the hashtag-based discussion forum? Drawing on the motivational theories of Self-Determination Theory and Social Influence Theory, as well as the concept of Personal Learning Environments, it was expected that online learners would be more motivated to participate in online discussions if they felt a sense of autonomy over the discussion, and if the discussion took place in an environment similar to the social media environment they experience in their personal lives. Participants in the course were undergraduate students in an educational technology course at a large Southeastern public university. Surveys were administered at the beginning and end of the semester to determine the participants’ patterns of technology and social media usage, attitudes toward social media and online discussion forums, and to determine motivation levels and social influence factors. -
Google+ Social Media Marketing Post-Campaign Report Campaign
Google+ Social Media Marketing Post-Campaign Report Campaign overview: The Google+ campaign for Kossine spanned over a period of 5 weeks from 12th April to 16th May. The key objective of the campaign was to establish Kossine’s social media presence, engage its current clientele online and attract new customers. We planned the posts in the beginning of each week and posted the content within the most active time range of 18:00 to 00:00. The Competition and Quiz questions were first prepared by us then were validated by Kossine’s professors. Constant meetings were held with the officials of Kossine to finalize Hangout dates and topics. We had numerous brainstorming sessions to formulate our strategies for the week and subsequently prepare posts. Course details posts were meticulously documented and were posted after permission from Kossine’s officials. Week 1: Our campaign head-started with company’s description post and a video highlighting Kossine’s rebranding. The profile photo and company details were updated. A Quiz question was posted everyday with the hashtag #KossQuiz. TechNewz Community was created wherein latest technical news were posted. Jokes with reference to programming were posted with the hashtag #JokeOfTheDay. After crossing 50 followers, we came up with our first Hangout which featured the founder of Kossine, who answered career counselling and company related questions. As slated, a practice competition was held acquainting the participants with the concept of a virtual treasure hunt. Subsequently, a Poll was conducted to judge user’s reaction. Week 2: This week saw an addition of quotes to posts with the hashtag #QuoteOfTheDay and some intriguing facts with the hashtag #JustAnotherFact. -
COVID-19 Twitter Monitor
COVID-19 Twitter Monitor: Aggregating and visualizing COVID-19 related trends in social media Joseph Cornelius∗ Tilia Ellendorff yz Lenz Furreryz Fabio Rinaldi∗yz ∗Dalle Molle Institute for Artificial Intelligence Research (IDSIA) ySwiss Institute of Bioinformatics zUniversity of Zurich, Department of Computational Linguistics fjoseph.cornelius,[email protected] ftilia.ellendorff, [email protected] Abstract Social media platforms offer extensive information about the development of the COVID-19 pandemic and the current state of public health. In recent years, the Natural Language Processing community has developed a variety of methods to extract health-related information from posts on social media platforms. In order for these techniques to be used by a broad public, they must be aggregated and presented in a user-friendly way. We have aggregated ten methods to analyze tweets related to the COVID-19 pandemic, and present interactive visualizations of the results on our online platform, the COVID-19 Twitter Monitor. In the current version of our platform, we offer distinct methods for the inspection of the dataset, at different levels: corpus- wide, single post, and spans within each post. Besides, we allow the combination of different methods to enable a more selective acquisition of knowledge. Through the visual and interactive combination of various methods, interconnections in the different outputs can be revealed. 1 Introduction Today, social media platforms are important sources of information, with a usage rate of over 70% for adults in the USA and a continuous increase in popularity.1 Platforms like Twitter are characterized by their thematic diversity and realtime coverage of worldwide events. -
Security Risks and Recommendations for Audio-Centric
Mind Your Voice: Security Risks and Recommendations for Audio-centric Social Media Platforms Technical Brief With an estimated growth rate of about half-million users per day, or 6 million users per month,1 ClubHouse is probably the most popular audio-only social network at the moment, albeit not the only one: Riffr,2 Listen,3 Audlist,4 and HearMeOut5 are some of the alternatives, while we've been waiting for the upcoming Twitter Spaces.6 There’s also Discord,7 which we didn't consider in our analysis because, despite having audio features, it's not centered around audio. This technical brief aims to provide users, app creators, and social network service providers practical security recommendations about the security risks brought forth by audio-only social networks. In parallel and independent from the research8 published by the Stanford Internet Observatory (SIO), we analyzed the apps (primarily ClubHouse but also including Riffr, Listen, Audlist, and HearMeOut). Among other attacks, we also found that, under some circumstances, an attacker can passively collect sensitive information about ongoing conversations, even "locked" ones; including participants, their identifiers, names, photos, and so on, without the need to join that room. This poses a clear privacy risk. Some security risks that we highlight here are unique to the ephemeral nature of audio-only social networks, while some are shared with other modern social network platforms (audio-centered or not). This research has been conducted in February 8-11 of this year. At the time of publication, some of the issues described in this document might have been or are currently being fixed by the app vendors. -
Separating Self-Expression and Visual Content in Hashtag Supervision
Separating Self-Expression and Visual Content in Hashtag Supervision Andreas Veit∗ Maximilian Nickel Serge Belongie Laurens van der Maaten Cornell University Facebook AI Research Cornell University Facebook AI Research [email protected] [email protected] [email protected] [email protected] Abstract previous hashtags by user: user A #livemusic #live #music #band #switzerland The variety, abundance, and structured nature of hash- #rock tags make them an interesting data source for training vi- sion models. For instance, hashtags have the potential to significantly reduce the problem of manual supervision and annotation when learning vision models for a large number of concepts. However, a key challenge when learning from hashtags is that they are inherently subjective because they are provided by users as a form of self-expression. As a previous hashtags by user: consequence, hashtags may have synonyms (different hash- user B #arizona #sport #climbing #bouldering #az tags referring to the same visual content) and may be poly- #rock semous (the same hashtag referring to different visual con- tent). These challenges limit the effectiveness of approaches that simply treat hashtags as image-label pairs. This paper presents an approach that extends upon modeling simple image-label pairs with a joint model of images, hashtags, and users. We demonstrate the efficacy of such approaches in image tagging and retrieval experiments, and show how the joint model can be used to perform user-conditional re- Figure 1. Image-retrieval results obtained using our user-specific trieval and tagging. hashtag model. The box above the query shows hashtags fre- quently used by the user in the past. -
Think Br Ie F Hashtag Standards for Emergencies
HASHTAG STANDARDS FOR EMERGENCIES OCHA POLICY AND STUDIES SERIES THINK BRIEF October 2014 | 012 This publication was developed by OCHA Field Information Services with the support of the OCHA Policy Development and Studies Branch (PDSB). This paper was written by Roxanne Moore and Andrej Verity. This publication was made possible with advice and support from Patrick Meier and Sarah Vieweg, from Qatar Computing Research Institute (QCRI). For more information, please contact: Policy Development and Studies Branch United Nations Office for the Coordination of Humanitarian Affairs (OCHA) E-mail: [email protected] These short think pieces are non-papers that present relatively new ideas that require testing and validation. The objective of the Think Brief is to generate feedback, views and advice. The Think Briefs do not necessarily represent the official views of OCHA. They are available on the OCHA website (www.unocha.org) and on ReliefWeb (www.reliefweb.org) under “Policy and Issues”. © OCHA, PDSB 2014 Background As part of the ongoing close collaboration between OCHA and QCRI, regular conversations on how to improve crisis computing have taken place over the past few years. In May 2014, members of OCHA and QCRI met in Doha to discuss our ongoing efforts and recognized that it is clear that innovations in policy were equally important as innovations in humanitarian technology. Standardization of social media (and data) hashtags and the encouragement of enabling GPS during crisis were recognized as a policy piece that could have major impact on integrating big-crisis data into emergency response going forward. This think brief is the culmination of the research. -
SNAPCHAT for NONPROFITS Using Snapchat to Drive Social Change
SNAPCHAT FOR NONPROFITS Using Snapchat to Drive Social Change. Snapchat has close to 178 million daily active users, who use the app on an average of more than 25 times per day. Snapchat is a mobile social media app that allows users to send short-lived photos and brief videos, called “Snaps,” to followers. It is an extremely visual and highly customizable platform that can turn small moments into a shareable story. Why Use Snapchat? Snapchat is ideal for organizations Connecting With looking to target younger audiences with If you have a lot of visual content you 85 percent of daily users aged 18 - 34. Your Audience would like to share, or if you frequently Once that you decide Snapchat is a good In fact, Snapchat is so popular with engage with your audience at in-person fit for your organization’s digital strategy young adults that 70 percent of 18- to events, Snapchat may be a good fit for and create an account, you can start 24-year-olds in the U.S. are already on the your organization. When considering any following users and begin to develop an platform. new social network, you need to determine audience of your own. if the platform will serve your strategic Individuals aren’t the only ones using You can drive followers to your new goals. Start by asking: Who are your target the app – more brands, businesses and Snapchat account by promoting it on your audiences, where can they be found and public figures use Snapchat than ever. other platforms. Posting exclusive content what do you want them to do? Nonprofits also are finding effective on Snapchat about contests or giveaways ways to use Snapchat. -
The Definitive Guide to the Most Influential People in the Online Dating Industry
THE DEFINITIVE GUIDE TO THE MOST INFLUENTIAL PEOPLE IN THE ONLINE DATING INDUSTRY WWW.GLOBALDATINGINSIGHTS.COM POWER LIST Sam Yagan Greg Blatt Sean Rad Michael S. Egan CEO Chairman President & co-founder CEO The Match Group The Match Group Tinder Spark Networks Tang Yan Noel Biderman Neil Clark Warren Andrey Andreev Founder & CEO Founder & CEO Founder & CEO Founder Momo Ashley Madison eHarmony Badoo Aaron Schildkrout/ Rose Gong Markus Frind George Kidd Brian Schechter Founder Founder & CEO Chief Executive Co-founders Jiayuan Plenty of Fish Online Dating Association HowAboutWe Cli Lerner Joel Simkhai Robyn Exton Ross Williams Founder & CEO Founder & CEO Founder & CEO CEO & co-founder SNAP Interactive Grindr Dattch White Label Dating Dave Heysen / Brett Harding / Justin McLeod Shayan Zadeh Daniel Haigh Laurence Holloway Founder & CEO Co-founder Co-founders Co-founders Hinge Zoosk Oasis Lovestruck 2 WELCOME & INTRODUCTION Welcome to the Power List 2015, Global Dating Insight’s guide to the most inuential people in the online dating industry. These are the people we believe are driving, shaping and developing this exciting industry. First of all, we have an admission - this is not a scientic process, and if we have made any omissions we apologise in advance. Each person on the following pages meets our strict criteria, they are a proven leader within their eld, with demonstrable power, inuence and budgetary clout. What does it mean to make the Power List? It means the individuals listed are the stars of our industry at this particular moment. Power cannot be determined by your ranking on Google, yet we know it when we see it. -
Social Media and Digital Communication Tools Training Notes
ONLINE TOOLS AND APPLICATIONS FOR NGOS This project is funded by the European Union. History of Internet and Social Media 1971: First e-mail ever sent. 1994: First Blog. 1995: MIRC and ICQ arrived. 1996: Ask.com search engine came into our lives. 1999: Blogger founded. 2000: Wikipedia, the free encyclopedia got published online 2003: Wordpress came about to compete with Blogger 2003: Myspace, LinkedIn founded – In 2006 Myspace was elected as the largest social network of the World. 2004: Facebook 2005: Flickr and YouTube founded – YouTube is the first large video storage and sharing site. 2006: Twitter came with 140 characters limit, increased the limit to 240 in 2018. 2007: A microblog site Tumblr founded. 2008: Facebook became the largest social network by overthrowing Myspace. 2009: Check-in concept got introduced by Foursquare. 2009: WhatsApp founded 2010: Instagram 2010: Google Buzz – on 5th of August 2011, Google has removed its Buzz service and directed users to Google+ 2011: Google Plus and Snapchat 2012: Pinterest, Vine and Tinder 2012: Facebook bought Instagram for 1 billion dollars 2013: Video sharing started on Instagram 2013: Medium came about as a blogging platform 2014: Facebook bought WhatsApp for 19 billion dollars 2015: Periscope got bought by twitter in 2015 even before it went online 2016: Vine – Closed 2016: Instagram introduced Story option and started competition with Snapchat 2017: TikTok 2019: Google+ closed Digital 2020: Turkey For the complete version of the research: wearesocial.com/digital-2020 Civic Space Project Survey – April 2020 Digital 2020: Cyprus For the complete version of the research: datareportal.com/reports/digital-2020-cyprus Digital April 2020 – Global For the complete version of the research: wearesocial.com/blog/2020/04/digital-around-the-world-in-april-2020 How to use Instagram effectively? Make sure to switch to business profile from the “settings” section and use statistics option offered by the business profile.