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1 Introduction
The rise of the Web has made it possible for anybody to create a website or a blog and to become a news medium. Undoubtedly, this was a hugely positive development as it elevated freedom of expression to a whole new level, thus allowing anybody to make their voice heard online. With the subsequent rise of social media, anybody could potentially reach out to a vast audience, something that until recently was only possible for major news outlets.
One of the consequences was a trust crisis: with traditional news media stripped off their gate-keeping role, the society was left unprotected against potential manipulation. The issue became a general concern in 2016, a year marked by micro-targeted online disinformation and misinformation at an unprecedented scale, primarily in connection to Brexit and the US Presidential campaign. These developments gave rise to the term "fake news", which can be defined as "false, often sensational, information disseminated under the guise of news reporting."111www.collinsdictionary.com/dictionary/english/fake-news It was declared Word of the Year 2016 by Macquarie Dictionary and of Year 2017 by the Collins English Dictionary.
In an attempt to solve the trust problem, several initiatives such as Politifact, Snopes, FactCheck, and Full Fact, have been launched to fact-check suspicious claims manually. However, given the scale of the proliferation of false information online, it became clear that it was unfeasible to fact-check every single suspicious claim, even when this was done automatically, not only due to computational challenges but also due to timing. In order to fact-check a claim, be it manually or automatically, one often needs to verify the stance of mainstream media concerning that claim and the reaction of users on social media. Accumulating this kind of evidence takes time, but time flies very fast, and any delay means more potential sharing of the malicious content on social media. A study has shown that for some very viral claims, more than 50% of the sharing happens within the first ten minutes after posting the micro-post on social media Zaman et al. (2014), and thus timing is of utmost importance. Moreover, an extensive recent study has found that "fake news" spreads six times faster and reaches much farther than real news Vosoughi et al. (2018).
A much more promising alternative is to focus on the source and to profile the medium that initially published the news article. The idea is that media that have published fake or biased content in the past are more likely to do so in the future. Thus, profiling media in advance makes it possible to detect likely "fake news" the moment it is published, by simply checking the reliability of its source.
From a practical perspective, political bias and factuality of reporting have not only a linguistic aspect but also a social context. Here, we study the impact of both, namely (i) what was written (the text of the articles published by the target medium, the text and the audio signal in the videos of its YouTube channel, as well as how the medium self-describes itself on Twitter) vs. (ii) who read it (by analyzing the media readers in Facebook, Twitter, and YouTube). We further study (iii) what was written about the target medium on Wikipedia.
Our contributions can be summarized as follows:
We model the leading political ideology (left, center or right bias) and the factuality of reporting (high, mixed, or low) of news media by modeling the textual content of what they publish vs. who reads it in social media (Twitter, Facebook, and YouTube). The latter is novel for these tasks.
We combine a variety of information sources about the target medium, many of which have not been explored for our tasks, e.g., YouTube video channels, political bias estimates of their Facebook audience, and information from the profiles of the media followers on Twitter.
We use features from different data modalities: text, metadata, and speech. The latter two are novel for these tasks.
We achieve sizeable improvements over the current state-of-the-art for both tasks.
We propose various ensembles to combine the different types of features, achieving further improvements, especially for bias detection.
We release the data, the features, and the code necessary to replicate our results.
In the rest of this paper, we discuss some related work, followed by a description of our system's architecture and the information sources we use. Then, we present the dataset, the experimental setup, and the evaluation results. Finally, we conclude with possible directions for future work.
2 Related Work
While leveraging social information and temporal structure to predict the factuality of reporting of a news medium is not new Canini et al. (2011); Castillo et al. (2011); Ma et al. (2015, 2016); Zubiaga et al. (2016), modeling this at the medium level is a mostly unexplored problem. A popular approach to predict the factuality of a medium is to check the general stance of that medium concerning already fact-checked claims Mukherjee and Weikum (2015); Popat et al. (2017, 2018). Therefore, stance detection became an essential component in fact-checking systems Baly et al. (2018b).
In political science, media profiling is essential for understanding media choice Iyengar and Hahn (2009), voting behavior DellaVigna and Kaplan (2007), and polarization Graber and Dunaway (2017). The outlet-level bias is measured as a similarity of the language used in news media to political speeches of congressional Republicans or Democrats, also used to measure media slant Gentzkow and Shapiro (2006). Article-level bias was also measured via crowd-sourcing Budak et al. (2016). Nevertheless, public awareness of media bias is limited Elejalde et al. (2018).
Political bias was traditionally used as a feature for fact verification Horne et al. (2018b). In terms of modeling, Horne et al. (2018a) focused on predicting whether an article is biased or not. Political bias prediction was explored by Potthast et al. (2018) and Saleh et al. (2019), where news articles were modeled as left vs. right, or as hyperpartisan vs. mainstream. Similarly, Kulkarni et al. (2018) explored the left vs. right bias at the article level, modeling both textual and URL contents of articles.
In our earlier research (Baly et al., 2018a), we analyzed both the political bias and the factuality of news media. We extracted features from several sources of information, including articles published by each medium, what is said about it on Wikipedia, metadata from its Twitter profile, in addition to some web features (URL structure and traffic information). The experiments on the Media Bias/Fact Check (MBFC) dataset showed that combining features from these different sources of information was beneficial for the final classification. Here, we expand this work by extracting new features from the existing sources of information, as well as by introducing new sources, mostly related to the social media context, thus achieving sizable improvements on the same dataset.
In follow-up work (Baly et al., 2019), we showed that jointly predicting the political bias and the factuality is beneficial, compared to predicting each of them independently. We used the same sources of information as in (Baly et al., 2018a), but the results were slightly lower. While here we focus on analyzing political bias and factuality separately, future work may analyze how the newly proposed features and sources affect the joint prediction.
3 System and Features
In this section, we present our system. For each target medium, it extracts a variety of features to model (i) what was written by the medium, (ii) the audience of the medium on social media, and (iii
) what was written about the medium in Wikipedia. This results in multi-modal (text, speech, and metadata) feature set, which we use to train a classifier to predict the political bias and the factuality of reporting of news media. Figure1 illustrates the system architecture.
3.1 What Was Written
We describe the features that we used to model the content generated by the news media, analyzing both the articles they publish on their website as well as relevant activity on social media.
3.1.1 Articles on the News Medium Website
Given a target news medium, we first collect a number of articles it has published. Then, we extract various types of features from the text of these articles. Below we describe these features in more detail.
Linguistic Features: These features focus on language use, and they model text structure, topic, sentiment, subjectivity, complexity, bias, and morality. They have proved useful for detecting fake articles, as well as for predicting the political bias and the factuality of reporting of news media Horne et al. (2018b); Baly et al. (2018a). We extracted such features using the News Landscape (NELA) toolkit Horne et al. (2018b)
, and we will refer to them as the NELA features in the rest of this paper. We averaged the NELA features for the individual articles in order to obtain a NELA representation for a news medium. Using arithmetic averaging is a good idea as it captures the general trend of articles in a medium, while limiting the impact of outliers. For instance, if a medium is known to align with left-wing ideology, this should not change if it published a few articles that align with right-wing ideology. We use this method to aggregate all features that we collected at a level of granularity that is finer than the medium-level.
Embedding Features: We encoded each article using BERT Devlin et al. (2019) by feeding the first 510 WordPieces222There is a limit of maximum of 512 input tokens, and we had to leave space for the special tokens [CLS] and [SEP]. from the article333This is recommended in Adhikari et al. (2019) when encoding full documents using Transformer-based models. and then averaging the word representations extracted from the second-to-last layer.444This is common practice, since the last layer may be biased towards the pre-training objectives of BERT.
In order to obtain representations that are relevant to our tasks, we fine-tuned BERT by training a softmax layer on top of the[CLS]
output vector to predict the label (bias or factuality) of news articles that are scrapped from an external list of media to avoid overfitting. The articles' labels are assumed to be the same as those of the media in which they are published (a form of distant supervision). This is common practice in tasks such as "fake news" detection, where it is difficult to manually annotate large-scale datasetsNørregaard et al. (2019). We averaged the BERT representations across the articles in order to aggregate them at the medium level.
Aggregated Probabilities: We represent each article by a
-dimensional vector that corresponds to its posterior probabilities of belonging to each class, of the given task, whether it is predicting the political bias or the factuality of the target news medium. These probabilities are produced by training a softmax layer on top of the [CLS] token in the above-mentioned fine-tuned BERT model. We averaged the probability representations across the articles in order to aggregate them at the medium level.
3.1.2 YouTube Video Channels
Some news media post their video content on YouTube. Thus, we use YouTube channels by modeling their textual and acoustic contents to predict the political bias and the factuality of reporting of the target news medium. This source of information is relatively underexplored, but it has demonstrated potential for modeling bias (Dinkov et al., 2019) and factuality Kopev et al. (2019).
Due to the lack of viable methods for automatic channel retrieval, we manually looked up the YouTube channel for each medium. For each channel marked as English, we crawled 25 videos (on average) with at least 15 seconds of speech content. Then, we processed the speech segments from each video into 15-second episodes by mapping the duration timeline to the subtitle timestamps.
We used the OpenSMILE toolkit Eyben et al. (2010) to extract low-level descriptors (LLDs) from these speech episodes, including frame-based features (e.g., energy), fundamental frequency, and Mel-frequency cepstral coefficients (MFFC). This set of features proved to be useful in the Interspeech Computational Paralinguistics challenge of emotion detection Schuller et al. (2009). To complement the acoustic information, we retrieved additional textual data such as descriptions, titles, tags, and captions. This information is encoded using a pre-trained BERT model. Furthermore, we extracted the NELA features from the titles and from the descriptions. Finally, we averaged the textual and the acoustic features across the videos to aggregate them at the medium level.
3.1.3 Media Profiles in Twitter
We model how news media portray themselves to their audience by extracting features from their Media Twitter profiles.
In our previous work, this has proven useful for political bias prediction Baly et al. (2018a). Such features include information about whether Twitter verified the account, the year it was created, its geographical location, as well as some other statistics, e.g., the number of followers and of tweets posted.
We encoded the profile's description using SBERT for the following reasons: (i) unlike the articles, the number of media profiles is too small to fine-tune BERT, and (ii) most Twitter descriptions have sentence-like structure and length. If a medium has no Twitter account, we used a vector of zeros.
3.2 Who Read it
We argue that the audience of a news medium can be indicative of the political orientation of that medium. We thus propose a number of features to model this, which we describe below.
3.2.1 Twitter Followers Bio
Previous research has used the followers' networks and the retweeting behavior in order to infer the political bias of news media F. M. F. Wong, C. W. Tan, S. Sen, and M. Chiang (2013); A. Atanasov, G. De Francisci Morales, and P. Nakov (2019); 40. Here, we analyze the self-description (bio) of Twitter users that follow the target news medium. The assumption is that (i) followers would likely agree with the news medium's bias, and (ii) they might express their own bias in their self-description.
We retrieved the public profiles of 5,000 followers for each target news medium with a Twitter account, and we excluded those with non-English bios since our dataset is mostly about US media. Then, we encoded each follower's bio using SBERT Reimers and Gurevych (2019). As we had plenty of followers' bios, this time fine-tuning BERT would have been feasible. However, we were afraid to use distant supervision for labeling as we did with the articles since people sometimes follow media with different political ideologies. Thus, we opted for SBERT, and we averaged the SBERT representations across the bios in order to obtain a medium-level representation.
3.2.2 Facebook Audience
Like many other social media giants, Facebook makes its revenues from advertisements. The extensive user interaction enables Facebook to create detailed profiles of its users, including demographic attributes such as age, gender, income, and political leaning. Advertisers can explore these attributes to figure out the targeting criteria for their ads, and Facebook returns an audience estimate based on these criteria. For example, the estimated number of users who are female, 20-years-old, very liberal, and interested in the NY Times is 160K. These estimates have been used as a proxy to measure the online population in various domains Fatehkia et al. (2018); Araujo et al. (2017); Ribeiro et al. (2018).
In this study, we explore the use of political leaning estimates of users who are interested in particular news media. To obtain the audience estimates for a medium, we identify its Interest ID using the Facebook Marketing API 555http://developers.facebook.com/docs/marketing-api. Given an ID, we retrieve the estimates of the audience (in the United States) who showed interest in the corresponding medium. Then, we extract the audience distribution over the political spectrum, which is categorized into five classes ranging from very conservative to very liberal.
3.2.3 YouTube Audience Statistics
Finally, we incorporate audience information from YouTube videos. We retrieved the following metadata to model audience interaction: number of views, likes, dislikes, and comments for each video. As before, we averaged these statistics across the videos to obtain a medium-level representation.
3.3 What Was Written About the Target Medium
Wikipedia contents describing news media were useful for predicting the political bias and the factuality of these media Baly et al. (2018a). We automatically retrieved the Wikipedia page for each medium, and we encoded its contents using the pre-trained BERT model.666Similarly to Twitter descriptions, the number of news media with Wikipedia pages is too small to fine-tune BERT. Similarly to encoding the articles, we fed the encoder with the first 510 tokens of the page's content, and used as an output representation the average of the word representations extracted from the second-to-last layer. If a medium had no page in Wikipedia, we used a vector of zeros.
4 Experiments and Evaluation
4.1 Dataset
We used the Media Bias/Fact Check (MBFC) dataset, which consists of a list of news media along with their labels of both political bias and factuality of reporting. Factuality is modeled on a 3-point scale: low, mixed, and high. Political bias is modeled on a 7-point scale: extreme-left, left, center-left, center, center-right, right, and extreme-right. Further details and examples of the dataset can be found in Baly et al. (2018a).
After manual inspection, we noticed that the left-center and right-center labels are ill-defined, ambiguous transitionary categories. Therefore, we decided to exclude news media with these labels. Also, to reduce the impact of potentially subjective decisions made by the annotators, we merged the extreme-left and extreme-right media with the left and right categories, respectively. As a result, we model political bias on a 3-point scale (left, center, and right), and the dataset got reduced to 864 news media. Table 1 provides statistics about the dataset.
|Political Bias
We were able to retrieve Wikipedia pages for 61.2% of the media, Twitter profiles for 72.5% of the media, Facebook pages for 60.8% of the media, and YouTube channel for 49% of the media.
4.2 Experimental Setup
We evaluated the following aspects about news media separately and in combinations: (i) what the target medium wrote, (ii) who read it, and (iii) what was written about that medium. We used the features described in Section 3 to train SVM classifiers for predicting the political bias and the factuality of reporting of news media. We performed an incremental ablation study by combining the best feature(s) from each aspect to obtain a combination that achieves even better results. We used 5-fold cross-validation to train and to evaluate an SVM model using different features and feature combinations. At each iteration of the cross-validation, we performed a grid search to tune the hyper-parameters of our SVM model, namely the values of the cost and of the value for the RBF kernel. In the process of search, we optimized for macro-average score, i.e., averaging over the classes, since our dataset is not balanced, which is true for both tasks. Finally, we evaluated the model on the remaining unseen fold. Ultimately, we report both macro- score, and accuracy.
We compared our results to the majority class baseline and to our previous work Baly et al. (2018a). The latter used (i) NELA features from articles, (ii) embedding representations of Wikipedia pages using averaged GloVe word embeddings, (iii) metadata from the media's Twitter profiles, and (iv) URL structural features. Since we slightly modified the MBFC dataset, we retrained the old model on the new version of the dataset.777The data and the corresponding code, both old and new, are available at https://github.com/ramybaly/News-Media-Reliability
To fine-tune BERT's weights, we trained a softmax layer on top of the [CLS] token of the pre-trained BERT model to classify articles for the task at hand: either predicting the articles' political bias as left, center, or right, or predicting their level of factuality as low or high.888We ignored mixed as it does not apply to articles. To avoid overfitting, we scrapped articles from news media listed in the Media Bias/Fact Check database, but not included in our dataset: 30K articles from 298 such media.
Finally, we used two strategies to evaluate feature combinations. The first one trains a single classifier using all features. The second one trains a separate classifier for each feature type and then uses an ensemble by taking a weighted average of the posterior probabilities of the individual models.
Note that we learn different weights for the different models, which ensures that we pay more attention to the probabilities produced by better models. We used the sklearn library to obtain probabilities from an SVM classifier as a function of the distance between the data point and the learned hyperplane using Platt scaling (for the binary case) or an extension thereof (for the 3-way case).
|Majority class
|Best model from Baly et al. (2018a)
|Articles: NELA
|Articles: BERT representations
|Articles: BERT probabilities
|Twitter Profiles: Sentence BERT
|YouTube: NELA (title, description)
|YouTube: OpenSmile (LLDs)
|. What
|YouTube: BERT (title, description, tags)
|Was Written
|YouTube: BERT (captions)
|Articles: ALL (c)
|Articles: ALL (en)
|Articles Twitter Prof. (c)
|Articles Twitter Prof. (en)
|Articles Twitter Prof. YouTube cap. (c)
|Articles Twitter Prof. YouTube cap. (en)
|Twitter Follower: Sentence BERT
|YouTube: Metadata
|Facebook: Political Leaning Estimates
|. Who
|Twitter Fol. YouTube Meta. (c)
|Read It
|Twitter Fol. YouTube Meta. (en)
|Twitter Fol. YouTube Meta. Facebook Estimates (c)
|Twitter Fol. YouTube Meta. Facebook Estimates (en)
|. What
|Was Written
|Wikipedia: BERT
|About the Medium
|All features: rows 3–11; 18–20; 25 (c)
|All features: rows 3–11; 18–20; 25 (en)
|AB: rows 12 & 21 (c)
|AB: rows 12 & 21 (en)
|AC: rows 12 & 24 (c)
|AC: rows 12 & 24 (en)
|ABC: rows 12, 21 & 24 (c)
|ABC: rows 12, 21 & 24 (en)
4.3 Political Bias Prediction
Table 2 shows the evaluation results for political bias prediction, grouped according to different aspects. For each aspect, the upper rows correspond to individual features, while the lower ones show combinations thereof.
The results in rows 3–5 show that averaging embeddings from a fine-tuned BERT to encode articles (row 4) works better than using NELA features (row 3). They also show that using the posterior probabilities obtained from applying a softmax on top of BERT's [CLS] token (row 5) performs worse than using average embeddings (row 4). This suggest that it is better to incorporate information from the articles' word representations rather than using [CLS] as a compact representation of the articles. Also, since our BERT was fine-tuned on articles with noisy labels obtained using distant supervision, its predictions for individual articles are also noisy, and so are the vectors of posterior. Yet, this fine-tuning seems to yield improved article-level representations for our task.
The results in rows 7–10 show that captions are the most useful type of feature among those extracted from YouTube. This makes sense since captions contain the most essential information about the contents of a video. We can further see that the BERT-based features outperform the NELA ones. Overall, the YouTube features are under-performing since for half of the media we could not find a corresponding YouTube channel, and we used representations containing only zeroes.
Rows 11-16 show the results for systems that combine article, Twitter, and YouTube features, either directly or in an ensemble. We can see on rows 13–16 that the YouTube and the Twitter profile features yield loss in performance when added to the article features (rows 11–12). Note that the article features already outperform the individual feature types from rows 3–10 by a wide margin, and thus we will use them to represent the What Was Written aspect of the model in our later experiments below.
We can further notice that the ensembles consistently outperform feature concatenation models, which is actually true for all feature combinations in Table 2.
|Majority class
|Best model from Baly et al. (2018a)
|Articles: NELA
|Articles: BERT representations
|Articles: BERT probabilities
|Twitter Profiles: Sentence BERT
|YouTube: NELA (title, description)
|YouTube: OpenSmile (LLDs)
|. What
|YouTube: BERT (title, description, tags)
|Was Written
|YouTube: BERT (captions)
|Articles: ALL (c)
|Articles: ALL (en)
|Articles: BERT Twitter Prof. (c)
|Articles: BERT Twitter Prof. (en)
|Articles: BERT Twitter Prof. YouTube: cap. (c)
|Articles: BERT Twitter Prof. YouTube: cap. (en)
|Twitter Follower: Sentence BERT
|YouTube: Metadata
|Facebook: Political Leaning Estimates
|. Who
|Twitter Fol. YouTube Meta. (c)
|Read It
|Twitter Fol. YouTube Meta. (en)
|Twitter Fol. YouTube Meta. Facebook Estimates (c)
|Twitter Fol. YouTube Meta. Facebook Estimates (en)
|. What
|Was Written
|Wikipedia: BERT
|About the Medium
|All features: rows 3–10; 17–19; 24 (c)
|All features: rows 3–10; 17–19; 24 (en)
|AB: rows 14 & 24 (c)
|AB: rows 14 & 24 (en)
|AC: rows 14 & 20 (c)
|AC: rows 14 & 20 (en)
|ABC: rows 14, 20 & 24 (c)
|ABC: rows 14, 20 & 24 (en)
Next, we compare rows 6 and 17, which show results when using Twitter information of different nature: from the target medium profile (row 6) vs. from the profiles of the followers of the target medium (row 17). We can see that the latter is much more useful, which confirms the importance of the Who Read It aspect, which we have introduced in this paper. Note that here we encode the descriptions and the self-description bio information using Sentence BERT instead of the pre-trained BERT; this is because, in our preliminary experiments (not shown in the table), we found the former to perform much better than the latter.
Next, the results in rows 20–23 show that the YouTube metadata features improve the performance when combined with the Twitter followers' features. On the other hand, the Facebook audience features' performance is deficient and hurts the overall performance, i.e., these estimates seem not to correlate well with the political leanings of news media. Also, as pointed by Flaxman et al. (2016), social networks can help expose people to different views, and thus the polarization in news readership might not be preserved.
Row 24 shows that the Wikipedia features perform worse than most individual features above, which can be related to coverage as only 61.2% of the media in our dataset have a Wikipedia page. Nevertheless, these features are helpful when combined with features about other aspects; see below.
Finally, rows 25–32 show the results when combining all aspects. The best results are achieved using the best features selected from each of the three aspects in an ensemble setting (row 32). This combination improves over using information from the article only (row 12) by +3.5 macro-points absolute. It further yields sizeable absolute improvements over the baseline system from Baly et al. (2018a): by +11.87 macro-macro- points. While a lot of this improvement is due to improved techniques for text representation such as using fine-tuned BERT instead of averaged GloVe word embeddings, modeling the newly-introduced media aspects further yielded a lot of additional improvements.
4.4 Factuality Prediction
Table 3 reports the evaluation results when using the proposed sources/features for the task of predicting the factuality of reporting of news media.
Similarly to the results for political bias prediction, rows 3–10 suggest that the features extracted from articles are more important than those coming from YouTube or from Twitter profiles, and that using BERT to encode the articles yields the best results. Note that overall, the results in this table are not as high as those for bias prediction. This reflects the level of difficulty of this task, and the fact that, in order to predict factuality, one needs external information or a knowledge base to be able to verify the published content.
The results in rows 11–16 show that combining the Twitter profile features with the BERT-encoded articles improves the performance over using the article text only.
Comparing rows 6 and 17 in Table 3, we can see that the Twitter follower features perform worse than using Twitter profiles features; this is the opposite of what we observed in Table 2. This makes sense since our main motivation to look at the followers' profiles was to detect political bias, rather than factuality. Moreover, the metadata collected from media profiles about whether the corresponding account is verified, or its level of activity or connectivity (counts of friends and statuses) are stronger signals for this task.
Finally, rows 25–32 show the results for modeling combinations of the three aspects we are exploring in this paper. The best results are achieved using the best features selected from the What was written and the What was written about the target medium aspects, concatenated together. This combination achieves sizeable improvements compared to the baseline system from Baly et al. (2018a): by +6.17 macro- points absolute. This result indicates that looking at the audience of the medium is not as helpful for predicting factuality as it was for predicting political bias, and that looking at what was written about the medium on Wikipedia is more important for this task.
5 Conclusion and Future Work
We have presented experiments in predicting the political ideology, i.e., left/center/right bias, and the factuality of reporting, i.e., high/mixed/low, of news media. We compared the textual content of what media publish vs. who read it on social media, i.e., on Twitter, Facebook, and YouTube. We further modeled what was written about the target medium in Wikipedia.
We have combined a variety of information sources, many of which were not explored for at least one of the target tasks, e.g., YouTube channels, political bias of the Facebook audience, and information from the profiles of the media followers on Twitter. We further modeled different modalities: text, metadata, and speech signal. The evaluation results have shown that while what was written matters most, the social media context is also important as it is complementary, and putting them all together yields sizable improvements over the state of the art.
In future work, we plan to perform user profiling with respect to polarizing topics such as gun control 40, which can then be propagated from users to media Atanasov et al. (2019); Stefanov et al. (2020). We further want to model the network structure, e.g., using graph embeddings 40. Another research direction is to profile media based on their stance with respect to previously fact-checked claims Mohtarami et al. (2018); Shaar et al. (2020), or by the proportion and type of propaganda techniques they use Da San Martino et al. (2019, 2020). Finally, we plan to experiment with other languages.
This research is part of the Tanbih project999http://tanbih.qcri.org/, which aims to limit the effect of "fake news," propaganda and media bias by making users aware of what they are reading. The project is developed in collaboration between the Qatar Computing Research Institute, HBKU and the MIT Computer Science and Artificial Intelligence Laboratory.
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- Using Facebook ad data to track the global digital gender gap. World Development 107, pp. 189–209. Cited by: §3.2.2.
- Filter bubbles, echo chambers, and online news consumption. Public opinion quarterly 80 (S1), pp. 298–320. Cited by: §4.3.
- Media bias and reputation. Journal of political Economy 114 (2), pp. 280–316. Cited by: §2.
- Mass media and american politics. SAGE Publications. Cited by: §2.
- Assessing the news landscape: a multi-module toolkit for evaluating the credibility of news. In Proceedings of the The Web Conference, WWW '18, Lyon, France, pp. 235–238. External Links: ISBN 978-1-4503-5640-4 Cited by: §2.
- Sampling the news producers: a large news and feature data set for the study of the complex media landscape. In Proceedings of the Twelfth International Conference on Web and Social Media, ICWSM '18, Stanford, CA, USA, pp. 518–527. Cited by: §2, §3.1.1.
- Red media, blue media: evidence of ideological selectivity in media use. Journal of communication 59 (1), pp. 19–39. Cited by: §2.
Detecting deception in political debates using acoustic and textual features.
Proceedings of the IEEE Automatic Speech Recognition and Understanding Workshop, ASRU '19, Singapore, pp. 652–659. Cited by: §3.1.2.
- Multi-view models for political ideology detection of news articles. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP '18, Brussels, Belgium, pp. 3518–3527. Cited by: §2.
Detecting rumors from microblogs with recurrent neural networks. In Proceedings of the 25th International Joint Conference on Artificial Intelligence, IJCAI '16, New York, NY, USA, pp. 3818–3824. Cited by: §2.
- Detect rumors using time series of social context information on microblogging websites. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM '15, Melbourne, Australia, pp. 1751–1754. External Links: ISBN 978-1-4503-3794-6 Cited by: §2.
- Automatic stance detection using end-to-end memory networks. In Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT '18, New Orleans, LA, USA, pp. 767–776. Cited by: §5.
- Leveraging joint interactions for credibility analysis in news communities. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM '15, Melbourne, Australia, pp. 353–362. External Links: ISBN 978-1-4503-3794-6 Cited by: §2.
- NELA-GT-2018: a large multi-labelled news dataset for the study of misinformation in news articles. In Proceedings of the International AAAI Conference on Web and Social Media, ICWSM '19, Munich, Germany, pp. 630–638. Cited by: §3.1.1.
- Where the truth lies: explaining the credibility of emerging claims on the Web and social media. In Proceedings of the 26th International Conference on World Wide Web Companion, WWW '17, Perth, Australia, pp. 1003–1012. External Links: ISBN 978-1-4503-4914-7 Cited by: §2.
- CredEye: a credibility lens for analyzing and explaining misinformation. In Proceedings of The Web Conference 2018, WWW '18, Lyon, France, pp. 155–158. Cited by: §2.
- A stylometric inquiry into hyperpartisan and fake news. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL '18, Melbourne, Australia, pp. 231–240. Cited by: §2.
- Sentence-BERT: sentence embeddings using Siamese BERT-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP '19, Hong Kong, China, pp. 3982–3992. Cited by: §3.2.1.
- Media bias monitor: Quantifying biases of social media news outlets at large-scale. In Proceedings of the Twelfth International AAAI Conference on Web and Social Media, ICWSM '18, Stanford, CA, USA, pp. 290–299. Cited by: §3.2.2.
- Team QCRI-MIT at SemEval-2019 task 4: propaganda analysis meets hyperpartisan news detection. In Proceedings of the 13th International Workshop on Semantic Evaluation, SemEval '19, Minneapolis, MN, USA, pp. 1041–1046. Cited by: §2.
- The INTERSPEECH 2009 emotion challenge. In Proceedings of the 10th Annual Conference of the International Speech Communication Association, INTERSPEECH '09, Brighton, UK, pp. 312–315. Cited by: §3.1.2.
- That is a known lie: detecting previously fact-checked claims. In Proceedings of the Annual Conference of the Association for Computational Linguistics, ACL '20, Seattle, WA, USA. Cited by: §5.
- Predicting the topical stance and political leaning of media using tweets. In Proceedings of the Annual Conference of the Association for Computational Linguistics, ACL '20, Seattle, WA, USA. Cited by: §5.
- [40] (2020) Unsupervised user stance detection on twitter. In Proceedings of the International AAAI Conference on Web and Social Media, ICWSM '20, Atlanta, GA, USA. Cited by: §3.2.1, §5.
- The spread of true and false news online. Science 359 (6380), pp. 1146–1151. Cited by: §1.
- Quantifying political leaning from tweets and retweets. In Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media, ICWSM '13, Boston, MA, USA, pp. 640–649. Cited by: §3.2.1.
- A bayesian approach for predicting the popularity of tweets. Ann. Appl. Stat. 8 (3), pp. 1583–1611. Cited by: §1.
- Analysing how people orient to and spread rumours in social media by looking at conversational threads. PLoS ONE 11 (3), pp. 1–29. Cited by: §2.


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Sativa Seeds
Sativa Seeds For Sale
Sativa Seeds at Seeds and Clones. Below is a list of Sativa Strains with 70% Sativa or more. All sativa cannabis seeds are categorized into Feminized Sativa Seeds, Autoflower Sativa Seeds and Regular Sativa Seeds.
List of Sativa Seeds by Strain
Feminized Sativa Seeds
- Atomical Haze Seeds (80% Sativa)
- Higher Understanding (Mostly Sativa)
- Laughing Buddha Seeds (80% Sativa)
- Super Silver Haze Seeds (90% Sativa)
Feminized Autoflower Sativa Seeds
- (80% Sativa)
- (80% Sativa)

subset: commoncrawl


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Cleveland Ostomy Association
P.O. Box 43677, Richmond Hts, Ohio 44143
~Established in 1960~
The Cleveland Ostomy Association is a volunteer, non-profit organization, dedicated to providing support, education, and information to those of all ages who have or will have an ostomy or a continent diversion procedure. We hope to bring ostomates, their families, and their friends the light of hope and the hand of friendship through visitation and rehabilitation.
Our chapter meetings are open to everyone. Support, sharing, caring, camaraderie, and education are what our meetings are all about. An ostomy nurse is present to answer any questions you may have. We need all ostomates and their families and friends to attend meetings. You never know when your story will help someone else. We all relate to different personalities, so bring yours and be an encouragement!
For more information call: 216-309-2977 or email us at email@example.com.
Please contact the group to request our newsletter or to get more information about our Zoom meetings by calling 440-347-0973.
Please call us if you need anything. We are still here for you!
Donating Unused Ostomy Supplies:
Locally you can drop off on the following porches:
29284 Willow Ln., Willoughby Hills 44092
7568 Tiber Ct., Macedonia 44056
For all drop-offs, please call 440-347-0973 before dropping off and put them in a plastic bag.
MedWish: (Drop off donations directly to them). Please find their phone number and address at www.medwish.org.
Contact Us
United Ostomy Associations of America
P.O. Box 525
Kennebunk, ME 04043-0525
Call us toll-free at: 1-800-826-0826.
Our Information Line hours are Monday-Friday, 9am to 3pm (Wednesday until 2pm) EST. If you have an emergency, please dial 911 or contact your local medical professional.
Please understand that UOAA is a private, nonprofit, advocacy and informational organization. We are not a medical facility and we do not have medical or legal professionals on staff. Therefore, UOAA does not provide Medical, Mental Health, Insurance or Legal Advice.
Get Involved
UOAA is the leading organization proactively advocating on behalf of the ostomy community. Recognizing that we are always stronger together, we encourage everyone to get involved by joining our Advocacy Network. We've also created several Advocacy Tools and Resources to help you successfully advocate on behalf of the ostomy community to ensure every ostomate receives quality care.
Latest News:
- Destined to Love, Ostomates by ChanceNovember 16, 2022 - 8:47 pm
- Ostomy Surgery Saved Matthew Perry's Life. It's Time to Stop the Stigmas.October 20, 2022 - 10:35 pm
- Best-in-Practice Care for Ostomy PatientsOctober 11, 2022 - 1:17 pm
- Diet and Digestion with an OstomyOctober 6, 2022 - 12:35 pm
- 6 Ways You (Yes, You) Can Make a Difference on Ostomy Awareness DaySeptember 30, 2022 - 5:48 pm
UOAA does not and shall not discriminate on the basis of race, color, religion (creed), gender, gender expression, age, national origin (ancestry), disability, marital status, sexual orientation, or military status, in any of its activities or operations.


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2020 Gold Rush Basketball Value Pack
3 cards per pack.
With basketball season reaching levels never before seen in terms of hype and excitement, Gold Rush is excited to announce the arrival of Gold Rush Basketball Value packs! Gold Rush Basketball Value Packs will return on February 7th, 2020 and will be sold in the 3 cards per pack. This collector-friendly product won't last long! So be sure to grab yours today before they're gone for good.
2020 Gold Rush Basketball Value Packs are loaded with the NBA's brightest rookies, stars and Hall of Famers! Each 3-card value pack will contain a combination of hits- such as autograph & memorabilia cards, graded cards, short-printed inserts and much more! Look for amazing hits from a wide variety of products in this great value!
All 30 NBA teams are included in this release.


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Scripture: John 1:1-18
Message: "Resolution of Realization"
John 1:1-18
The Word Became Flesh
This is the word of God, for the people of God. Thanks be to God.
Prayer of Confession: Almighty and Merciful Father, we are sorry that we have not rejoiced in Your way, Your will, and Your Word. We sometimes deceive ourselves and think that You're not actively living among us. Help us to now know, what You already know. Open us, bare us, and reveal our sins now to Your merciful forgiveness. In Your name Jesus we pray, Amen.
Pastor Joe will be available at the church on Thursday afternoon from 2 to 4. If you need to speak to him, contact Pastor Joe at 570-267-4570 (cell) or Email: firstname.lastname@example.com
Social Hour TODAY following church. Greeting cards for signature are in the Community Building.
Loose change goes to General Fund
Sunday School starts at 8:30am
Choir practice will be on Thursday at 1:00! Please come a join us in singing praises to Jesus!
Corporation Meeting after church on January 9, 2022
PW will meet on January 13th at 11:00 A.M.
Men's Breakfast Wednesday January 19th at 8:00 A.M.
Session Meeting Saturday, January 22nd at 9:00 A.M.
Newsletter Deadline – Tuesday, January 25th, 2022.
Please sign up to host one of the social hours on the Sunday after Communion Service on the first Sunday of the month.
Listen to WPEL 96.5 for church cancellation if your internet is down during a storm this winter.
Pastor Joe and Bonne want to thank you for your Christmas gift, your support, and your love over the past year. May 2022 be filled with good health, God's blessings and a deeper relationship with God.


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About Us
The Friends of Old St Paul's Incorporated was formed in 1965 to initially save the then-to-be-demolished St Paul's Cathedral in Wellington New Zealand.
We now continue to assist to raise the profile and help the public to enjoy this wonderful historic wooden church, now run and managed by Heritage New Zealand.
People we work with
The Friends of Old St Paul's are proud to work alongside Heritage New Zealand Pouhere Taonga who care for Old St Paul's.
The volunteers, who are essential to help with all aspects of what we do, cannot be thanked enough. Without them, we would not be able to operate.
We are also pleased to acknowledge and thank organisations who have so generously supported our work recently:
- The Adam Foundation
- Wellington City Council Creative Communities Fund
- New Zealand Post
As well as those who have donated through collections or personally and by bequest.


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How does childhood memory work and what do we remember from childhood?
Have you ever wondered why you don't remember being a baby? Or why you can easily remember the lyrics to a song you learned as a teenager, even though it was 20 (or more) years ago? The answers to these [look more]


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Add this shop to my favorites. Antique Map of Carthage by Shaw (1773). Availability: in stock Condition : Very good, general age-related toning. Date : 1773 Overall size : 21 x 26 cm.
Image size : 15.5 x 22 cm. Description: Antique print titled'Kaart van de Legging van het Oude Karthago'. Old map of the city of Carthage and the Bay of Carthage.It was the center or capital city of the ancient Carthaginian civilization, on the eastern side of the Lake of Tunis in what is now the Tunis Governorate in Tunisia. Originates from the first Dutch editon of an interesting travel account of Northern Africa titled'Reizen en Aanmerkingen door en over Barbaryen en het Ooste'. Artists and Engravers: Author: T. Boddaert and published by J. This item is in the category "Antiques\Maps, Atlases & Globes\Africa Maps". The seller is "bartele-gallery" and is located in this country: NL. This item can be shipped worldwide.


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Is it a symbol only you came across, or is it depicted in a circuit where you could tell it's gender!P or N?
white cat in a snowstorm?Its same symbol like this (but on this image its just zoomed out)
know either.
Yes. The same symbol is used in a whole bunch of different circuit (pseudo) schematics that I'm looking at; hence I didn't want to post a complete circuit as I don't want the discussion to be about any particular circuit.the problem is, it's not marked for polarity... it's neither N-channel, or P-channel... it's either a printing error, or it's something that doesn't exist. the dot showing the connection between the source and bulk is actually fairly commonly shown in print.
Actually its white cat hidden in white box during snowstorm.white cat in a snowstorm?
Sorry for sarcasm. Maybe i have seen that symbol in some book. EU book. But no, i do not remember where-Nor does it look exactly like any of these:
View attachment 134127
Nor these:
View attachment 134128
Nor these:
View attachment 134129
In fact, it doesn't match ANY symbol on ANY of these pages of symbols:
View attachment 134131
And I'd already checked that lot BEFORE posting.
The "wise guy" isI think it is some wise guy
On my hand writed schematics i am using just T placed inside circle for transistor, i am to lazy to draw it corectly.


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Why Turn to RAC for Edison Professional Speakers in Fenton?
When it comes to finding a party speaker in Fenton, buying a system outright can require major cash. Want new speakers like yesterday? Rent-to-Own Edison Professional speaker systems in Fenton at your nearest Rent-A-Center. One-touch Bluetooth technology in many Edison Professional audio systems gives you all the fun without the clunky wires and cables. If you need to get an Edison Professional speaker in Fenton fast, you've come to the right stop. With RAC, you can fill out a no-credit** order online now! Then, stop by your local Fenton RAC to get your stuff. You might even score same-day speaker pickup at most RAC locations!**
Power Up with Edison Professional PA Speakers in Fenton, MO
Sometimes, your party faces a last-minute change of plans. So, what do you do when you need a sound system that transports easily across Fenton and beyond? Rent an Edison Professional PA system, of course! Travel-friendliness and functionality meet in this rent-to-own Edison Professional speaker. Suitcase-inspired wheels and an extendable handle allows you to easily move it from pre-party to after-party so you can keep the vibe going wherever you are. Ready to get your karaoke on? Be the best host with by plugging the mic or pair a Bluetooth device with the speaker to start jamming out wirelessly. When you need to rent a speaker set in Fenton, for your next party or event, head to Rent-A-Center for Edison Professional Bluetooth speakers for rent in Fenton, MO.
Find Edison Professional Speakers for Rent in Fenton, MO
Your party mix merits the most reliable Edison Professional speakers you can rent! But why do layaway or take out credit when you can rent with no credit history needed**? A rent-to-own Edison Professional sound system from your local RAC gives you the best of everything! Choose from 2500W and 4500W options for your rent-to-own speaker system. With a remote control, an equalizer and fader functions, and a high-tech LED display, your next get-together or karaoke night will be complete with your new sound system from Edison Professional. Not sure where to find an Edison Professional Bluetooth speaker for rent in Fenton, MO? Find your nearest Rent-A-Center in Fenton, where we can answer your questions about speaker options.
Rent-A-Center: Party Central for Rent-to-Own Edison Professional Speakers
Thing RAC only rents major appliances and furniture? Think again! From extra furniture to Edison Professional speakers for rent in Fenton, RAC rents what you need to celebrate every special occasion. And who has the time to save up for a huge upfront payment? Not you! At Rent-A-Center, we have no credit** payment options that work with your life. Want to know how you can rent-to-own Edison Professional Bluetooth speakers for rent in Fenton? Shop online or come to your nearest Fenton Rent-A-Center now to start the process.


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Santo Domingo
We've compiled the best of the best in Santo Domingo - browse our top choices for the top things to see or do during your stay.
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Get FREE email communications from Fodor's Travel, covering must-see travel destinations, expert trip planning advice, and travel inspiration to fuel your passion.
We've compiled the best of the best in Santo Domingo - browse our top choices for the top things to see or do during your stay.
The castle of Don Diego Colón, built in 1517, was the home to generations of the Christopher Columbus family. The Renaissance-style structure...Read More
The coral-limestone facade of the first cathedral in the New World (Catedral Primada de América) towers over the south side of the Parque Colón...Read More
The Ladies Street" was named after the elegant ladies of the court: in the Spanish tradition, they promenaded in the evening. Here you can see...Read More
This combination church and fortress, the only one of its kind in Santo Domingo, was completed in 1562. It is a fine example of colonial Spanish...Read More
Founded in 1510, this graceful building is still a Dominican church and convent. Note the prominent and beautiful rose window. In 1538, Pope...Read More
Constructed between 1512 and 1544, the St. Francis Monastery contained the church, convent, and hospital of the Franciscan order. Sir Francis...Read More
The National Pantheon (circa 1714) was once a Jesuit monastery and later a theater. The real curiosity here is the military guard, who stays...Read More
The huge statue of Christopher Columbus in the park named after him dates from 1897 and is the work of sculptor Ernesto Gilbert. At the far...Read More
Calle Las Damas dead-ends as it goes in the direction of the Malecón. Few people ever make it past the junction of Calle Padre Bellini, thinking...Read More
This wide esplanade, which goes past the Casas de Reales in front of Don Diego Columbus's former palace, El Alcazar de Colón, is the area in...Read More
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TE8227XXWL Hot sequins letters mesh trumpet sleeve cute doll tops
Product Detail:
COLOR: White, Pink
Fabric: Cotton + Mesh
WEIGHT: 0.270kg
SIZE: One size
LENGTH: 48cm, Bust: 88cm, Shoulder: 36cm
Maxuce is one of the earliest korean fashion style clothing wholesale suppliers and manufacturer in China which was established in 2008. We have been concentrated to providing customers with excellent quality womens fashion clothing. By sell online, we are able to provide all the products to anyone who need them in very cheap price.
Email us: firstname.lastname@example.com


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Create a Website Account - Manage notification subscriptions, save form progress and more.
Beaver County Commission Meetings are now streamed live on our YouTube Channel.
Viewers can watch live or view the recorded meeting later.
Click to Watch
RRCI's (Red Rock Center for Independence) mission is to empower people with disabilities in southwestern Utah to live independently through educations, personalized services, and technology.
Contact infomation for Millard and Beaver counties is li...


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PPC Advertising Agency
Akus digital Solution is PPC advertising Agency in Nairobi, Kenya that Delivers Positive Results. Choose our google advertising services and see your brand reach more customers. Grow your business with our data-driven paid search advertising campaigns.
PPC Advertising Services You Can Trust.
At Akus Digital Solutions, we are committed to providing you with the best Paid Search Advertising services in Kenya. Talk to us and find out how you can complement your SEO efforts with data-driven PPC advertising. We understand the benefits of an effective paid advertising strategy to your overall digital marketing strategy.
Google Advertising Agency that Create Repeat Customers.
Choose our google advertising agency and reach more customers
PPC advertising is a powerful data-driven digital marketing strategy to increase your conversions rates and maximize on ROI. The advantage of PPC in advertising is that it creates incredible opportunities for your brand to directly connect with your target audience. Akus' PPC advertising services help you reach your buyers on the paid platforms that are most cost-effective. From Google Ads to Facebook ads, we create PPC ads strategies that get our clients the best results.
Why Choose Akus Digital Solution as Your PPC Advertising Company?
By choosing Akus digital solution's PPC advertising services, you can expect the following.
Advanced Keyword Research
We target the right audience to save you more money while making the most of the available PPC ads budget.
Effective Ads Copy Resources.
Writing effective PPC ads content is not just a walk in the park. At Akus Digital Solution, we go the extra mile to create ads copies that create a good first impression to your audience.
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Did you know that optimizing your landing page decreases up to 30% of your PPC ads cost? At Akus Digital Solution, we carry out regular A/B testing for landing pages which is essential to increasing your overall conversion rate.
Dedicated PPC ads Manager
Our PPC ads managers have the expertise and experience to manage multiple ads campaigns at a go and make constant improvements for optimal results. Whatever your needs, whether it is drawing quality traffic or optimizing landing pages for sales, your dedicated manager will address your needs
Detailed Monthly Reporting
At Akus Digital solution, we keep you up-to-date with how your PPC ads campaigns are performing. We explain every data in detail and how they interpret your business goals and objectives.
Transparent PPC ads Pricing
We love to form a strong and trustworthy working relationship with our clients. This is why transparency is one of our key values. We tell it from the start if our PPC services are a good fit for your business based on your needs and goals.
Our Process
We research and analyze your industry trends what works and what doesn't. We identify a starting keyword list and other opportunities.
Based on your target audience, goals and needs we develop buyer personas that provide detailed information about your audience's preferences, lifestyle choices, and behaviors.
LANDING PAGE OPTIMIZATIOn
We help you optimize your landing page with quality content and appropriate call to action buttons.
Upon the successful development of the ad strategy, we help you launch it across various channels and platforms. Proper timing is taken into account when posting to achieve the best results.
We closely monitor and review the campaign. Information gathered from the review is critical in making improvements to the campaign.
👇FAQs About PPC Advertising Agency👇
Still have questions? It's ok please feel free to contact us we'd be happy to help.
What is PPC in Advertising?
👉 PPC (pay-per-click) is an internet marketing strategy that involves paying a fee every time an ad link is clicked. In other words, it's an advertising method in which you pay for your website visits (Traffic) rather than getting those visits organically.
The PPC advertising model is offered primarily by search engines (e.g., Google, Bing, Yahoo) and social networks (e.g., Facebook, Instagram, Youtube).
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👉PPC advertising benefits can help your business achieve several marketing goals more quickly than other digital marketing strategies. The benefits of PPC advertising to businesses include; 1) cost-effectiveness, 2) High-level brand visibility, 3) driving targeted traffic, 4) eCommerce sales, 5) growing your business.
Contact Akus Digital Solution or Call us at +254 771 616 234 to speak with a PPC Advertising Agency Expert to learn more about the benefits of PPC advertising.
How much does google Advertising cost?
👉 Google advertising allows you to set an advertising budget that fits your business goals and needs. Google advertising cost doesn't have to be that expensive. Maximizing Google ads requires analytical and research skills, constant testing, and offering quality and relevant content. Google advertising agency can help you learn how to advertise on google and grow your business from Google ads.
What are the google advertising services charge?
👉 When businesses invest in PPC advertising, it is always advisable to partner with a PPC agency. A PPC advertising Agency like Akus Digital solution has the expertise, skill, and resources, to develop, launch, and manage your paid advertising campaigns on platforms such as Google ads in the most effective way.
We offer Google Advertising Agency services monthly management fee that ranges from Ksh 35,000 to Ksh. 150,000, not including your monthly ads spend.
What is the benefit of PPC advertising agency services?
👉 Using a PPC advertising agency services saves you time and money by having experts who have the technical know-how of running the campaign the right way. In simple words, your ads will perform more successfully without ads budget wastages.
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👉 Akus Digital solution helps you create a winning PPC strategy for eCommerce. From Google shopping ads to social media ads, we are here to increase your ROI and improve overall conversion with PPC advertising for eCommerce businesses.
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Why Choose AKUS Digital Solution
Our primary objective is to help your business grow and build your brand. It's what we're passionate about, and we know how to make it work for our client.
We're a digital marketing agency that seeks to understand our client's business goals. Akus digital solutions are customized based on individual client's goals and needs.
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We use the latest technology to position your business as a top player in your industry and surpass all competitors.
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Transparent Communication
We fully understand the value of open & timely communications. You are speaking directly to our digital experts.
We Value Integrity
We value honesty and integrity and expect the same from our clients. We work with a sincere heart in helping you find success.


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Now, the price of dumbbells will vary depending on how heavy they are. With that said though, these Bowflex ones are great because you can adjust the dumbbells from 5 pounds up to 52.5 pounds.
It can be unnerving when you lose weight without knowing why. You need to eat a certain amount of calories to maintain your weight. The average calories an adult needs per day is 2,000. That amount can vary based on your specifics, like activity level and other factors. .
Some people balk when they hear that olive oil can aid in the weight loss process, but the types of fat included in olive oil, particularly oleic acid, trigger feelings of satiety in your body, reducing your chances of overeating. Besides, the beneficial omega-3 fats in olive oil will help increase your energy levels and reduce bad cholesterol from your system. [23]
The Mediterranean diet is taking a page out of the eating patterns of those in the Mediterranean, which includes high intakes of plant-based foods. Although nothing is technically off limits, this diet recommends only eating poultry, eggs, cheese, yogurt, and red meat in moderation.
It also comes with a monitor display that tells you how many kilometers or miles you've rowed, the time you've spent on the machine, and how many seconds it takes to row one metre, alongside other valuable data.
The authors discovered that using analytical language as opposed to present-focused language was related to better weight loss and a reduced risk of quitting in dialogues regarding goal-striving, such as when discussing attempts to pursue a goal with a coach. According to new research, persons who used analytical language saw greater weight reduction and a lower dropout rate
Eating at regular times during the day helps burn calories at a faster rate. It also reduces the temptation to snack on foods high in fat and sugar. 3. Eat plenty of fruit and veg
Graves' disease is an autoimmune disease that affects the thyroid. Some of the symptoms of Graves' disease include hand tremors, rapid heartbeat, trouble sleeping, enlarged thyroid, thinning of the skin or fine brittle hair. Causes of Graves' disease are thought to be multifactorial such as genes, gender, stress, and infection. Treatment for Graves' disease is generally medication.
"When people think, 'If I can will myself to not be hungry, to not have cravings, to control what I eat every moment of every day, I will lose the weight and keep it off,' it's like saying, 'If I just concentrate hard enough, my blood sugar levels will become normal,'" Dr. Jastreboff says. People need to know that there are physiological reasons why that strategy doesn't work, she adds. "Obesity is a complex metabolic disease with a clear biological basis, and we can treat it with targeted therapy aimed at the biology."
It is also important to be physically active. A good goal for many people is to work up to exercising for at least 30 minutes, 4 to 6 times a week. Regular exercise helps you burn calories faster, even when you are sitting still. Exercise also helps you burn fat and build muscle.
Unintentional weight loss is when you lose weight without changing your diet or exercise routine. It can be a sign of stress or a serious illness. See a GP just in case. Causes of unintentional weight loss
The American Heart Association recommends the following dietary guidelines, which are suitable for most adults.
You definitely don't want to use it when you're pregnant or nursing, or if you have liver problems. It is possible that manic symptoms may emerge as a side effect.
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Abbreviations: MAOI = monoamine oxidase inhibitor, CNS = central nervous system, SSRI = selective serotonin-reuptake inhibitors, SNRI = selective serotonin-norepinephrine reuptake inhibitors. **Weight change relative to placebo (95 percentile confidence interval) using intent-to-treat analyses for each medication at 1 year. No studies for older noradrenergic agents (phentermine, diethylpropion, phendimetrazine, and benzphetamine) met inclusion criteria for length of treatment, sample size, and attrition. a Medications listed on Drug Enforcement Administration Schedule IV are associated with a lower risk of abuse than & Common adverse events for noradrenergic agents include those listed as common in the NIDDK Weight-control Information Network Fact Sheet "Prescription Medications for the Treatment of Obesity"100 as adverse event frequency is not available in the drug package inserts for these agents. For orlistat, lorcaserin, and phentermine/topiramate ER, common adverse events are those listed in the drug package inserts that are reported to occur more frequently than placebo and with more than 5% prevalence.
Esophageal cancer is a disease in which malignant cells form in the esophagus. Risk factors of cancer of the esophagus include smoking, heavy alcohol use, Barrett's esophagus, being male and being over age 60. Severe weight loss, vomiting, hoarseness, coughing up blood, painful swallowing, and pain in the throat or back are symptoms. Treatment depends upon the size, location and staging of the cancer and the health of the patient.
© 2022 Organic Information Services Pvt Ltd. All the information on this website is for education purpose only. Consult a medical practitioner for health problems. Images are provided by Shutterstock & our contributors. Organic Facts may receive a portion of revenues if you click on the sponsored ads and links by Google, Ezoic, or the Amazon Affiliate program.


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Simple steps we can take to support our loved ones when transitioning into a senior living or special care facility to ease them into their new living arrangement without stress.
How to support a family member when transitioning into an assisted living facility
When caring for an elderly, we worry about their well-being constantly, after all this is why supported assistance care homes come into consideration, after there has been a well thought decision to move our beloved ones into one of these facilities, there are more things to follow up. In this article we discuss some actions we recommend taking to relieve the stress that may accompany this transition.
Consider Hiring a cleaning service for senior to make the moving an easier experience
Moving is always a confusing and overwhelming experience, let alone for the elder person who will be leaving behind possessions that have been part of their life and identity for a very long time, most of the physical work required is definitely too much of a shore to be tackled by a person that is already in need of support, family members will be keen to help out and try to declutter and separate important family heirlooms for the safekeeping, this may end up being a much more intense experience than what was expected and could end up making this transition more painful than it has to be, for this reason we recommend considering a service that specializes in elderly care and if the case requires it there are also companies who specialize in hoarding cleanups.
Get a mental health expert or trained professional to monitor their emotional well-being
During this time of adjustment to their new environment and routine it is very possible that our loved ones will face depression and loss of interest due to the uncertainty that changing their way of living can bring, for the elderly their space and their belongings hold a very strong meaning and letting go is a very emotional process.
When speaking about professionals that can help us provide this well needed assistance, we can mention the better known professionals such as a therapist who is a trained expert in matters of psychological well-being and of course in strategies to make the transition easier forboth the family and the elderly throughout the process of moving into a senior living residence, but we can also provide alternative options that approach the same matter through a different method, one example of this would be an elder care doula who is a nonmedical professional that has been trained to give a holistic physical, emotional and spiritual aid to the elderly.
If possible, hire a care assistant to help with settling into their new home
Usually, a care assistant is a person who will take care of the day-to-day tasks for the elderly but another role that can be taken by an assistant is helping us ensure our loved ones are able to fall into a new routine without forgetting to take their medications, schedule and following-up on their medical appointments even help them with personal hygiene whenever needed. As expected, some senior care homes already provide this service, but it might be a good idea to provide a more personalized support at least during the few first months if the residence provided is more of an independent living sort of arrangement, especially because in this case it can help seniors get acquainted with their community and the services outside the facility such as their nearest supermarket, mailing offices and the nearest medical facilities.
To summarize, moving into a senior living home or a specialized care facility can be a daunting and stressful experience not only for our loved ones but for the family, and it can become an upsetting experience for our senior family members who will be facing a major change in their lives, this can trigger several negative emotional responses which can be avoided if we take steps to ensure mental and emotional well-being for them. There are trained professionals who have the knowledge and experience to facilitate the transition and help us make this already challenging event into a more organized, safe and comforting situation.


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